PY Prashant Yadav
Senior Solutions Architect · 17+ Years · Data · Agentic AI · Cloud · Edge

Connecting application, data & AI architecture into one governed platform.

Prashant Yadav architects large-scale cloud platforms, enterprise data architectures, and agentic AI / GenAI systems — across enterprise, government, and defense environments.
From hybrid lakehouses (S3 · Glue · MWAA · Redshift · Kafka) and multi-agent RAG platforms on AWS Bedrock and Azure OpenAI, to multi-tenant Kubernetes serving 1M+ users, air-gapped defense DevSecOps, and ARM edge AI pipelines — the focus is always delivery: scalable, secure, and production-ready.
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17+
Years across infrastructure, cloud, data architecture, agentic AI, and enterprise platforms — Feb 2009 to today.
1M+
Users supported through enterprise-grade Kubernetes LMS platforms.
76
Universities covered through ICAR national LMS deployment.
70%
Reduction in provisioning time on Siemens vSoC CI/CD platform.
15+
Enterprise architectures delivered — data lakehouse, agentic AI, Kubernetes, edge AI, automotive simulation, and DevSecOps.
9
Roles across 5 organizations — sustained progression from L2 support to Senior Manager Solutions Architect.
Prashant Yadav
About

Architecture depth with hands-on delivery

Senior Solutions Architect with 17+ years architecting large-scale cloud platforms, enterprise data architectures, agentic AI / GenAI systems, DevSecOps ecosystems, and distributed systems across enterprise, government, and defense environments.
Strength is connecting application architecture, data architecture, and AI orchestration into one scalable, governed enterprise platform — instead of treating them as separate silos. Expertise spans solution & data architecture, multi-agent AI orchestration, RAG pipelines, Kubernetes platforms, ARM virtualization, and edge computing.
Architecture
Solution Architecture Data Architecture Enterprise Architecture Cloud Architecture Architecture Governance RFP Solution Design
Agentic AI & GenAI
Agentic AI & GenAI Multi-Agent Orchestration RAG Pipelines AWS Bedrock Azure OpenAI SageMaker Vector Databases OpenSearch · Pinecone · FAISS Prompt Engineering LLM Evaluation Hallucination Mitigation AI Guardrails Model Versioning & Drift
Data Platform & Analytics
Data Lakehouse AWS Glue · Crawlers · ETL Apache Airflow / MWAA Apache NiFi Redshift · Athena Kafka · Kinesis · Firehose Apache Spark Data Lineage & Catalog Schema Evolution Data Governance PII Masking · RBAC
Cloud & Platform
AWS (EC2, EKS, VPC, IAM) Kubernetes / EKS AWS Lambda · S3 · Route53 CloudWatch · API Gateway · KMS Hybrid Cloud Helm Multi-Tenant Platforms Terraform / IaC Docker Nutanix HCI Disaster Recovery Cloud Cost Optimization
DevSecOps & CI/CD
DevSecOps CI/CD Pipelines Jenkins · SonarQube · Trivy Nexus · Gitea · Tuleap Air-Gapped Platforms Azure AD · Keycloak SSO
Edge AI & Embedded
Edge AI ARM Virtualization IoT Core · IoT Greengrass TensorFlow Lite Computer Vision Digital Twin CARLA Simulation
Data, Messaging & Observability
PostgreSQL MongoDB Redis MQTT Prometheus · Grafana ELK / OpenSearch Azure / O365
Programming & AI-Assisted
Python (Automation) React.js ChatGPT · Claude · Copilot Vendor Management
Work Experience

17+ years of professional growth

A consistent track record across 9 roles since Feb 2009 — from IT support and BPO infrastructure through cloud engineering, into senior solution architecture leadership in data, AI, and platform engineering.
Sep 2025 – Present
Noida
Judge India Solutions Pvt. Ltd.
Senior Manager Solutions Architect
Architecting enterprise data platforms — S3 lakehouse (Raw / Silver / Gold), Redshift warehouse, AWS Glue + MWAA Airflow pipelines, and streaming ingestion via Kafka, Kinesis, and IoT Core.
Designing agentic AI / GenAI platforms on AWS Bedrock and Azure OpenAI — multi-agent orchestration with classification, retrieval (RAG), summarization, compliance, and response agents.
Building RAG pipelines on vector databases (OpenSearch, Pinecone, FAISS) with chunking, embeddings, prompt guardrails, and tenant-scoped retrieval.
Implementing enterprise AI governance — PII masking, tenant isolation, prompt registry, model versioning, drift monitoring, eval harness, audit logging, and human-in-loop validation.
Solving production GenAI challenges — retry storms and queue buildup mitigated using queue isolation, circuit breakers, dead-letter queues, retry backoff, and graceful degradation.
Architecting digital twin and simulation platforms for automotive and defense systems with AWS-based vSoC environments (ARM + GPU), secure VPCs, CI/CD, and automation.
Delivering ADAS simulation pipelines using CARLA, ROS2, and distributed compute frameworks; defense-grade DevSecOps with Jenkins, Gitea, Nexus, and Ansible.
Oct 2023 – Dec 2025
Noida
Judge India Solutions Pvt. Ltd.
Solutions Architect
Delivered Kubernetes-based national LMS platform across 76 universities, supporting 1M+ users with multi-tenant deployment and centralized governance on Nutanix HCI.
Designed Visionext ARM Edge AI pipeline integrating AWS IoT Core, Greengrass, Lambda, S3, SageMaker, MQTT, and MongoDB for real-time analytics.
Built CI/CD pipelines with Jenkins, SonarQube, and Trivy; automated multi-environment (DEV / UAT / PROD) deployments improving release stability.
Implemented cloud monitoring (Prometheus, Grafana, CloudWatch, ELK / OpenSearch), HA architecture, and cost-optimization strategies across business units.
Architected ARM virtualization integrated with AWS IoT Core, IoT Greengrass, Lambda, and SageMaker for distributed real-time analytics and edge intelligence.
Jun 2022 – Sep 2023
Noida
Judge India Solutions Pvt. Ltd.
Senior Technology Architect
Led enterprise infrastructure architecture and compliance delivery (ISO, CMMI), acting as architecture design authority across POCs, RFPs, and enterprise implementations.
Designed HA systems on AWS with auto-scaling, multi-AZ failover, and disaster recovery — leveraging EC2 snapshots, S3 backup storage, and automated failover strategies.
Containerized workloads using Docker and optimized deployment pipelines; led production deployment for the Tata Sky Self-Help Portal with Docker-based CI/CD automation.
Implemented Azure Active Directory, Keycloak SSO, AWS IAM, and KMS encryption strengthening enterprise authentication, identity governance, and access control.
Sep 2019 – Jun 2022
Noida
Judge India Solutions Pvt. Ltd.
Deputy Manager – Information Technology
Led enterprise IT infrastructure modernization and cloud migration initiatives across multiple business units.
Executed migration of Exchange to Office 365 and on-prem servers to Azure cloud — modernizing identity architecture and reducing operational overhead.
Designed and implemented Business Continuity Planning (BCP) frameworks during pandemic disruptions — ensuring zero downtime for distributed teams.
Managed network infrastructure, firewalls, VPNs, ISP coordination, and IT asset lifecycle governance across multi-site deployments.
Optimized cloud spending through monitoring, resource rightsizing, and utilization analytics — achieving measurable cost reductions.
Aug 2016 – Oct 2019
Noida
Judge India Solutions Pvt. Ltd.
Assistant Manager – IT
Managed enterprise IT operations including networking, firewalls, and system deployment for 200+ employees across multiple offices.
Ensured IT policy implementation, security controls, and seamless business support across delivery, AI/ML, sales, and operations teams.
Directed vendor evaluation and strategic procurement negotiations — ensuring optimized infrastructure procurement and operational continuity.
Collaborated cross-functionally with delivery, AI/ML, sales, and operations teams to align IT capability with business needs.
Aug 2012 – Oct 2016
Noida
Geetarsh Solutions Pvt. Ltd.
Director & Freelance IT Consultant
Led end-to-end setup of BPO and IT infrastructure for 4+ organizations — operational readiness, dialers, VOIP, networking hardware, and CRM integrations.
Designed scalable infrastructure deployment blueprints supporting inbound and outbound technical-support business models.
Directed 15–20 member technical teams as Working Director — overseeing backend technology operations and structured service delivery.
Managed vendor procurement, hardware sourcing, and technology budgeting — ensuring cost-controlled operational scalability.
Conducted hands-on team training programs establishing structured IT workflows and sustainable processes.
Jun 2010 – Aug 2012
Noida
Dell Perot Systems TSI India
Sr. Client Support Associate
Delivered enterprise-grade IT asset and application support for Dell employees globally — ensuring SLA-driven incident resolution workflows.
Managed BMC Remedy incident management system maintaining structured ticketing, escalation, and request-lifecycle governance.
Led transition of US hardware support operations to India — strengthening cross-regional knowledge transfer capabilities.
Led knowledge-base migration improving documentation accuracy and internal troubleshooting efficiency.
Supported enterprise mobile device management and internal software deployment across distributed corporate environments.
Recognized for consistent high-quality technical resolution contributing to operational efficiency improvements.
Aug 2009 – May 2010
Noida
Cyfuture India Pvt. Ltd
L2 Technician
Supervised 10+ L1 resources managing PPC and SEO technical-sales business performance metrics across two operational locations.
Monitored revenue attainment, service-quality benchmarks, and process-optimization initiatives.
Designed customized training modules supporting technical-sales transitions and structured onboarding methodologies.
Managed daily operational KPIs improving customer satisfaction and reducing business-process inefficiencies.
Facilitated transition of two technical processes ensuring structured documentation and workflow continuity.
Contributed to business growth through data-driven operational improvement strategies.
Feb 2009 – Jul 2009
Noida
IGATE Global Solutions
Sr. Technical Support Analyst
Provided dedicated technical support services for Royal Bank of Canada internal enterprise users.
Administered McAfee Endpoint Security portal — managing endpoint protection policy configurations across the secure banking environment.
Supported enterprise mobile devices and application installations across secure banking environments.
Key Expertise

Major areas of technical impact

A structured view of the architecture domains and transformation areas that define the professional journey.

Enterprise Platform Engineering

Architected multi-tenant Kubernetes (EKS) platforms supporting 1M+ users across 76 universities with high availability and scalability.
Enabled zero-downtime deployment strategies and reliable rollout processes across enterprise environments.
Led digital platform modernization with focus on resilience, governance, and operational stability.

Cloud, DevSecOps & Infrastructure Leadership

Designed production cloud workloads using AWS EKS, Lambda, S3, VPC, IoT Core, SageMaker, and ECR.
Built secure CI/CD pipelines using Jenkins, SonarQube, Trivy, approval gates, and release governance frameworks.
Established observability via Prometheus, Grafana, AWS CloudWatch, and infrastructure health dashboards.

Data Bridge Platform — AWS Data Fusion & Governed Lakehouse

Unified 5+ fragmented finance and staffing systems into a governed S3 Medallion Lakehouse (Bronze/Silver/Gold) using AWS Glue PySpark ELT, CDC via AWS DMS, and incremental load with Glue Job Bookmarks — enabling schema evolution and raw data retention for reprocessing.
Automated document extraction (Amazon Textract async + SNS), entity resolution in PySpark across HR, CRM, and payroll systems, and calculated KPIs (billing margin, PO balance, approval lag) with Glue Data Quality validation at every layer.
Layered Amazon Bedrock RAG on top — Titan Embeddings v2 (1536-dim), OpenSearch HNSW+BM25 hybrid retrieval with security trimming, and Claude 3 Sonnet generating grounded answers with invoice/PO/timesheet citations. QuickSight SPICE dashboards with IAM row-level security for finance managers.

GenAI & RAG Production Systems — HR Policy RAG + LMS AI Platform

Built an enterprise HR Policy RAG Assistant: structure-aware chunking (300–500 tokens, 50–80 overlap at section boundaries), Titan Embeddings v2, OpenSearch field-level security trimming DURING retrieval (never post), Bedrock Claude 3 Sonnet at temperature 0.1 with citation enforcement per factual claim and <5% hallucination target on ground-truth eval sets.
Designed a 7-microservice AI layer for a national LMS (1M+ learners, 76 universities): Bedrock content generation, Polly Neural TTS (8 Indian languages), Transcribe+Step Functions video enrichment pipeline (moderation→transcription→WebVTT→chunking→embedding→indexing), SageMaker A/B recommendations, and Rekognition pre-publication moderation with AIMS/ISO 42001 audit trail.
Implemented enterprise AI governance across both platforms: Bedrock Guardrails (prompt injection + PII output), IAM tenant-scoped roles, per-tenant KMS CMKs, VPC PrivateLink (no public internet), human-in-loop SQS review queues, CloudWatch per-tenant budget caps with API Gateway throttling on breach.

Edge AI & Embedded Innovation

Built edge-to-cloud intelligent pipelines using YOLO, MQTT, Lambda, object storage, and event-based processing.
Worked on Raspberry Pi, NXP i.MX, Arduino UNO Q, and ARM Virtual Hardware for embedded AI use cases.
Focused on lightweight inference and NPU-oriented optimization for practical edge deployment.

Simulation & Digital Twin Systems

Developed digital twin and validation environments for robotics, edge systems, and virtual testing workflows.
Integrated real-time video pipelines using RTSP, Kinesis, and OpenCV for analytics and visualization.
Delivered automotive simulation using CARLA, ROS2, and ARM CSS workload migration for Siemens.
Projects

Enterprise-scale project delivery

Hands-on delivery across defense, automotive, education, agriculture, and enterprise cloud environments — from architecture through production.
Data Bridge Platform · AWS
AWS-Native Data Fusion · Finance & Staffing Intelligence
Unified fragmented finance and staffing data from 5+ operational systems into a governed AWS Lakehouse — S3 Bronze/Silver/Gold, Glue PySpark ELT, CDC via AWS DMS, Amazon Textract for document extraction, entity resolution, and natural-language querying via Amazon Bedrock Claude 3 + OpenSearch HNSW+BM25 hybrid RAG with cited, grounded responses and IAM row-level security.
S3 Medallion AWS Glue PySpark Amazon Textract OpenSearch Hybrid Bedrock RAG Lake Formation
HR Policy RAG Assistant · AWS
Enterprise RAG · Amazon Bedrock · OpenSearch · Textract
Built an enterprise HR Policy RAG Assistant enabling employees to ask natural-language questions about policies and SOPs — receiving cited, role-aware answers. Textract async ingestion, structure-aware 300–500 token chunking, Titan Embeddings v2 (1536-dim), OpenSearch hybrid BM25+k-NN, field-level security trimming DURING retrieval, and Bedrock Claude 3 Sonnet at temperature 0.1 with citation enforcement and less than 5% hallucination target.
Amazon Bedrock Titan Embeddings v2 OpenSearch HNSW Security Trimming Structure-Aware Chunking Bedrock Guardrails
LMS AI Content Generation · AWS
1M+ Learners · 76 Universities · Bedrock, Polly, Transcribe, SageMaker
Designed AI layer for a national LMS serving 1M+ learners across 76 universities — 7 independent AI microservices on Kubernetes: content generation (Bedrock Claude 3), audio synthesis (Polly Neural TTS, 8 Indian languages), video transcription + WebVTT captions (Transcribe + Step Functions), translation (Translate + Custom Terminology), content moderation (Rekognition), RAG Gateway (OpenSearch Serverless + Bedrock KB), and personalised recommendations (SageMaker Shadow Variants). Human-in-loop SQS review queue, AIMS/ISO 42001 audit trail, per-tenant KMS CMKs, 40–60% authoring time reduction.
Amazon Bedrock Amazon Polly Amazon Transcribe Amazon Translate SageMaker Rekognition Step Functions AIMS/ISO 42001
Siemens / CES 2026
Automotive vSoC Cloud Platform · ARM Graviton + GPU
Architected AWS-based vSoC simulation platform for Siemens CES 2026 — ARM Graviton4 EC2 (Prod/QA/Dev) + GPU nodes for CARLA/ROS2 simulation workloads. Multi-stage CodePipeline with ARM cross-compilation, Amazon ECR, Trivy CVE scanning, and deployment to EKS. VPC PrivateLink isolation, KMS encryption, IAM least-privilege, CloudWatch + X-Ray observability. Achieved 70% provisioning time reduction vs. on-prem. Enabled cloud-embedded convergence validation for international automotive demonstrations.
AWS ARM Graviton4 GPU Workloads Kubernetes CodePipeline VPC PrivateLink CARLA ROS2
Siemens / CES 2026
ARM CSS Automotive Migration · ADAS Simulation Pipeline
Led migration of ADAS simulation workloads to ARM CSS architecture for Siemens CES 2026. Built distributed CARLA + ROS2 simulation pipeline with SageMaker for sensor-fusion model training and inference. Delivered embedded-and-cloud convergence validation — ARM firmware and cloud-scale simulation running on the same architecture family, enabling real-time closed-loop testing of autonomous driving stacks at scale.
ARM CSS CARLA Simulator ROS2 SageMaker ADAS Validation Embedded-Cloud Convergence
DRDO
Air-Gapped DevSecOps Platform
Designed secure DevSecOps stack using Tuleap, Jenkins, Gitea, SonarQube and Nexus in air-gapped defense infrastructure. Automated ARM GNU 64 cross-compilation pipelines with RBAC and LDAP-based governance controls.
Tuleap Jenkins Gitea RBAC LDAP
ICAR
National Multi-Tenant LMS Platform
Architected Kubernetes clusters on Nutanix HCI supporting 1M+ users across 76 universities. Built CI/CD automation enabling scalable multi-tenant deployments with centralized governance.
Kubernetes Nutanix HCI 1M+ Users 76 Universities
Visionext
ARM Edge AI Surveillance Platform
Designed ARM edge AI pipeline integrating AWS IoT Core, Greengrass, Lambda, S3 and SageMaker for real-time analytics. Implemented MQTT image pipeline with SageMaker inference and MongoDB for real-time processing.
AWS IoT Core Greengrass SageMaker MQTT MongoDB
AI Summit PoC
Hybrid Edge AI Agriculture System
Engineered MPU+MCU deterministic Edge AI architecture using TensorFlow Lite for crop vs weed detection. Implemented real-time actuation with MQTT telemetry benchmarking inference latency.
TensorFlow Lite MPU+MCU MQTT Edge AI
AI Summit Speaker
Distributed Intelligence Mesh
Presented heterogeneous workload orchestration comparing single SBC and MPU+MCU architectures. Demonstrated live failover micro cluster with deterministic workload isolation.
Workload Orchestration SBC Micro Cluster
Embedded World 2025
Edge Cloud Showcase
Demonstrated AWS IoT Greengrass and Lambda-based distributed edge intelligence platform at international exhibition in Nuremberg, Germany.
IoT Greengrass Lambda Edge Intelligence
Enterprise
Disaster Recovery – AWS DR Framework
Designed AWS disaster recovery architecture leveraging EC2 snapshots, S3 backup storage and automated failover strategies for enterprise business continuity.
EC2 Snapshots S3 Backup Failover
Enterprise
O365 & Azure Migration
Migrated Exchange, File Server and Active Directory to Office 365 and Azure modernizing identity architecture for enterprise workforce continuity.
Office 365 Azure AD Identity
Tata Sky
Self-Help Portal & Containerization
Led production deployment for Tata Sky Self Help Portal and implemented Docker-based CI/CD automation improving deployment reliability and release speed.
Docker CI/CD Production
UKSC
Cloud-Native LMS Deployment
Deployed Kubernetes-based LMS platform with automated DEV-UAT-PROD pipelines improving release stability and reducing manual deployment overhead.
Kubernetes DEV/UAT/PROD Pipelines
Interview Preparation

Generic interview Q&A by category

12 categorised question sets covering Data Fusion, GenAI & RAG, Agentic AI, AWS Architecture, Security, Kubernetes Architecture, Kubernetes HA & Resilience, Ingress & Load Balancing, Cloud Architecture, Solution Architecture, and Scenario-Based questions. Use the download links below for the full deep-dive libraries.
Data Fusion & Lakehouse
ELT, Medallion Architecture & Data Governance
ETL vs ELT? ETL transforms before loading — rigid, schema-on-write. ELT loads raw to S3 first, transforms inside the platform — flexible, enables schema evolution and raw reprocessing.
What is a Medallion architecture? Bronze (raw, immutable), Silver (cleaned Parquet), Gold (curated business entities with KPIs). Each zone serves different consumers with increasing data quality.
Why Parquet? Columnar format — 10× faster analytics queries vs CSV, better compression, schema evolution. Standard for Silver/Gold S3 zones.
How do you handle schema evolution? AWS Glue Data Catalog schema versioning. Apache Iceberg / Delta Lake for backward-compatible column additions. Crawlers auto-update the catalog on each run.
What is CDC and why use it? Change Data Capture via AWS DMS — tracks row-level changes, propagates only changed records. Eliminates full-table scans, enables near-real-time downstream freshness.
How do you implement data governance? AWS Lake Formation for column/row-level access on Glue Catalog tables. IAM Identity Center for SSO. CloudTrail for immutable API audit. KMS CMK per tenant. PII masking before any AI call.
GenAI & RAG
Retrieval-Augmented Generation, Embeddings & Production RAG
What is RAG and why use it over fine-tuning? RAG grounds LLM answers in retrieved enterprise data — two pipelines: Ingestion (chunk→embed→index) and Query (embed→retrieve→generate). Policies change frequently; RAG updates by reindexing — zero retraining cost.
What is an embedding? A 1536-dim numerical vector (Titan Embeddings v2) representing semantic meaning. Similar content is geometrically close — enables similarity search beyond keyword matching. Same model MUST be used for both ingestion and query.
What is hybrid search? BM25 keyword scoring + HNSW k-NN vector similarity in one OpenSearch query. BM25 handles exact terms (invoice numbers, policy codes); vector handles semantic meaning. More accurate than either alone.
How do you prevent hallucination? Context-grounded prompts, relevance threshold fallback (no answer if evidence insufficient), citation enforcement per factual claim, Bedrock Guardrails output screening, continuous ground-truth eval (target <5% hallucination).
What is security trimming in RAG? Filtering retrieval results by user access permissions DURING the OpenSearch query — not post-retrieval. Unauthorised content never enters the LLM context window. Implemented via OpenSearch field-level security.
Why structure-aware chunking? Splits at section boundaries (not arbitrary token counts). 300–500 tokens, 50–80 overlap. Prevents a policy rule being separated from its exception. Metadata: policy name, section, effective date, access_classification.
Agentic AI
Multi-Agent Architecture, ReAct, Tool Calling & Human-in-Loop
Chatbot vs agent? Chatbot responds to single turns. Agent plans, selects tools (Lambda functions), executes multi-step tasks, evaluates intermediate results, and continues dynamically until a goal is achieved — ReAct loop (Reason→Act→Observe).
What is the ReAct pattern? Reasoning + Acting. Agent alternates: Thought (reason about current state) → Action (invoke tool) → Observation (tool result) → next Thought. Amazon Bedrock Agents implements this as a managed service.
How do you handle retry storms in agentic systems? Queue isolation per agent type via SQS, circuit breakers with half-open state, exponential backoff with jitter, dead-letter queues for failed tasks, graceful degradation to partial answers.
When do you add Human-in-Loop (HITL)? Before AI output is published (LMS quizzes/translations), before an agent takes an irreversible action, when confidence score falls below threshold, and for all regulated AI decisions requiring human accountability.
How do you evaluate an agentic system? Task completion rate, citation accuracy, hallucination rate on ground-truth test sets, tool call correctness, latency per agent hop, cost per successful completion, and human reviewer override rate.
What are Bedrock Guardrails? Safety layer for Bedrock API. Input: prompt injection detection, topic blocking, jailbreak detection. Output: PII detection, toxic content screening. Configurable per tenant and use case. Applied before and after generation.
AWS Architecture
Core AWS Services, Equivalents & Design Decisions
Azure Data Factory equivalent? AWS Glue for ETL jobs, Data Catalog, and Crawlers. Amazon EventBridge for event-driven triggers. Glue Job Bookmarks = ADF incremental load. Glue Data Quality = ADF validation activities. AWS DMS for CDC.
Azure Document Intelligence equivalent? Amazon Textract. Both extract text, tables, key-value pairs from PDFs/scans. Textract: AnalyzeDocument (sync) and StartDocumentAnalysis (async + SNS completion). Confidence scores logged to DynamoDB.
Azure AI Search equivalent? Amazon OpenSearch Service. Both support BM25 keyword + HNSW k-NN vector + semantic search. Both support metadata filtering and field-level security. OpenSearch integrates with Bedrock; Azure AI Search with Azure OpenAI.
When to use Step Functions vs SQS? Step Functions for multi-step workflows with branching, retries, error handling, and state (e.g. video enrichment pipeline). SQS for simple point-to-point task queues, decoupling producers and consumers (e.g. AI review queues).
What is AWS Lake Formation? Fine-grained column, row, and tag-based access control on Glue Data Catalog tables. Works with Athena, Redshift Spectrum, Glue ETL. Replaces bucket-level S3 RBAC for data-level security — finance managers can't access HR PII columns even in the same Gold table.
How do you design for multi-tenancy? IAM tenant-scoped roles, tenant_id in every JWT and AI invocation, OpenSearch field-level security trimming by tenant_id, per-tenant KMS CMKs, CloudWatch per-tenant budget caps with API Gateway throttling on breach.
Security & Governance
IAM, Encryption, Compliance & AI Governance
How do you handle data residency for government clients? All AI invocations from ap-south-1 (Mumbai) and ap-south-2 (Hyderabad) only. PII masked before any AI call. VPC PrivateLink for all services — data never traverses public internet. Contractual data residency clause in MSA.
Azure Key Vault equivalent on AWS? AWS splits it: Secrets Manager for credentials (API keys, DB passwords) with 30-day auto-rotation. KMS for CMK encryption keys. K8s External Secrets Operator syncs Secrets Manager secrets into Kubernetes Secret objects.
What is AIMS/ISO 42001? AI Management System standard governing AI lifecycle: risk assessment, model versioning, audit trails, human oversight, incident handling. Every AI invocation logged: model version, cost, reviewer action, correlation ID.
How do you prevent prompt injection? Bedrock Guardrails input screening, Lambda pre-processing sanitisation, least privilege tool scoping (agents only have access to tools they need), and structured output schemas to prevent instruction-following exploits.
What is VPC PrivateLink? Assigns a private IP to a managed service inside a VPC — eliminating public internet exposure. Supports S3, Bedrock, OpenSearch, Glue, Textract, Transcribe, and 100+ AWS services. All three projects use PrivateLink exclusively.
How do you implement audit trails for AI? CloudTrail for all AWS API calls (immutable). CloudWatch logs every AI invocation with tenant_id, model version, prompt hash, cost, latency. DynamoDB for quality evaluation tables and policy version tracking. Required for AIMS/ISO 42001.
System Design & Trade-offs
Architecture Decisions, Scalability & Production Patterns
How do you scale RAG to millions of documents? OpenSearch Serverless auto-scales capacity units. HNSW ef_construction tunes index quality vs build speed. Batch vectorisation with Titan Embeddings API. Separate ingestion and query clusters for workload isolation.
When do you fine-tune vs use RAG? RAG for frequently changing enterprise knowledge (policies, documents) — zero retraining cost. Fine-tuning for output format, tone, and style adaptation. Use both: RAG for knowledge, fine-tuning for style consistency.
How do you handle long-context documents? Structure-aware chunking at section boundaries. Metadata-rich chunks for filtered retrieval. Context budget allocation: reserve tokens for system prompt, top-3 chunks, and response. Bedrock Claude 3 Sonnet: 200K context window.
How do you design for zero-downtime deployments? Blue-green Kubernetes namespaces (DEV/UAT/PROD). Istio canary traffic shifting. Helm chart versioning. Feature flags for gradual AI feature rollout. Step Functions wait states for human approval before PROD promotion.
What is entity resolution and when do you need it? Matching records referring to the same real-world entity across multiple systems (same employee with different IDs in HR vs CRM). Implemented in PySpark on Glue using deterministic key matching. Essential for cross-system data fusion.
How do you design for zero-downtime deployments? Blue-green Kubernetes namespaces (DEV/UAT/PROD). Istio canary traffic shifting. Helm chart versioning. Feature flags for gradual AI feature rollout. Step Functions wait states for human approval before PROD promotion.
Kubernetes Architecture
Cluster Components, Scheduling, Probes & Enterprise Patterns
When would you NOT choose Kubernetes? For a small application with low change frequency, a managed app service or serverless platform may be cheaper and simpler. Kubernetes provides flexibility, but that flexibility introduces operational complexity — don't choose it just because it's popular.
Deployment vs StatefulSet vs DaemonSet? Deployment for stateless replicas with rolling updates. StatefulSet for stable identities and persistent volumes (databases). DaemonSet for one pod per node — logging agents, security agents, storage drivers. Prefer managed databases outside the cluster for critical enterprise data.
Resource requests vs limits? Request = CPU/memory Kubernetes reserves for scheduling. Limit = maximum allowed. CPU over limit is throttled; memory over limit causes OOM kill. Setting all requests equal to high limits wastes capacity; setting too low causes overcommitment and node pressure. Base requests on observed usage.
Liveness vs readiness vs startup probes? Readiness removes pod from Service endpoints (no restart). Liveness restarts a stuck container. Startup protects slow-starting apps from premature liveness failures. A liveness check should only fire when restart actually helps — don't make it dependency-heavy or you get cascading restarts.
How do you structure namespaces in enterprise? Separate by environment, product, tenant, or ownership. Each namespace gets ResourceQuota, LimitRange, NetworkPolicy, RBAC, PodSecurity, cost labels. For strong regulatory isolation, use separate clusters — namespaces are a logical, not security, boundary.
Helm vs Kustomize? Helm for reusable charts with configurable values and dependency management. Kustomize for environment-specific overlays on plain manifests without a template language. Practical: Helm for third-party charts, Kustomize for org-specific environment config.
Kubernetes HA & Resilience
Control Plane, etcd, Rolling Updates & Failure Patterns
What does K8s HA actually require? More than just multiple replicas. Full HA covers: control-plane redundancy, etcd quorum, worker node redundancy, replica distribution across zones, LB redundancy, storage HA, DNS resilience, autoscaling, PodDisruptionBudgets, observability, and tested recovery procedures — all measured against business SLOs and RPO/RTO.
Why does etcd require an odd number of members? etcd uses Raft consensus requiring majority quorum. 3 members tolerate 1 failure; 5 tolerate 2. 4 members still only tolerate 1 — the extra member adds cost without increasing fault tolerance. High disk/network latency or co-locating a majority in one zone undermines the design regardless of count.
What is a PodDisruptionBudget? Limits replicas that may be voluntarily unavailable during node drain, upgrade, or autoscaler consolidation. For 3 replicas: minAvailable: 2. PDBs don't protect against involuntary failures (node crash) and can't help with single-replica workloads or insufficient cluster capacity.
A deployment causes downtime despite 3 replicas — what's wrong? maxUnavailable too high; pods marked ready before truly usable; all replicas on one node/zone; slow LB target registration; non-graceful shutdown; incompatible DB migration; no capacity for surge pods; session state stored locally; new version failed under real traffic.
HPA adds pods but performance worsens — why? More pods can overload a constrained dependency: database connection limits, external API throttling, cache misses, lock contention, or NAT Gateway saturation. Check end-to-end saturation. Sometimes the answer is backpressure, caching, or a hard maxReplicas — not unlimited pod creation.
How do you handle DB schema changes in zero-downtime deploys? Expand-and-contract: first add backward-compatible schema changes; deploy code that works with both versions; migrate/backfill data; switch reads/writes; only delete old columns after all old app versions are gone. Long migrations separate from pod startup. Monitor locks, replication lag, and transaction impact.
Ingress & Load Balancing
AWS ALB/NLB, NGINX, TLS, Service Mesh & Troubleshooting
Layer 4 vs Layer 7 load balancing? L4 routes TCP/UDP by IP+port — fast, protocol-agnostic, preserves end-to-end protocol. L7 understands HTTP/HTTPS, routes by host/path/header, can terminate TLS, integrate WAF, and do content-aware routing. Choose based on protocol, routing complexity, static IP needs, and security controls.
AWS ALB vs NLB vs Gateway LB? ALB: L7 HTTP/HTTPS, host/path routing, OIDC auth, WAF integration. NLB: L4 TCP/UDP/TLS, static IPs, source-IP preservation, high-throughput/low-latency. Gateway LB: inserts virtual network appliances (firewalls, IPS, DPI) into traffic paths transparently.
Ingress vs Gateway API? Ingress: basic HTTP routing, limited extensibility, controller-specific annotations. Gateway API: explicit GatewayClass/HTTPRoute/TCPRoute resources, separates infrastructure-owner vs app-team responsibilities, richer routing, better portability. For new enterprise platforms, evaluate Gateway API first.
NGINX Ingress vs cloud-native controller? NGINX when you need consistent behavior across clouds/on-prem or detailed proxy config. Cloud-native when deep integration with managed LB, WAF, certificates, and target registration reduces ops effort. Trade-off: portability vs provider integration.
Troubleshoot a 502/503 from ingress? Trace layer by layer: DNS + cert validity → LB listener/routing rule → target health + security rules → ingress controller logs → Service selector + endpoint list → pod readiness + app logs → port/protocol alignment → timeout + connection limits → NetworkPolicy. 502 = invalid upstream response; 503 = no healthy backend.
Does a service mesh replace an ingress controller? No. A mesh manages service-to-service traffic inside the cluster (mTLS, retries, traffic splitting, telemetry). An ingress controller manages traffic entering the cluster from outside. A mesh ingress gateway can play both roles, but doesn't eliminate the need for edge protection. Introduce mesh only when its value justifies operational complexity.
Cloud Architecture
Well-Architected, Landing Zones, Multi-Tenancy & Cost
How do you approach a new cloud architecture? Start with business outcomes, user patterns, integrations, data classification, compliance, availability, latency, RTO/RPO, and budget. Build a current-state view. Design covers identity, network, compute, data, security, observability, deployment, resilience, and cost governance. Validate risky assumptions with focused PoCs before committing.
What are the well-architected pillars? Operational excellence, Security, Reliability, Performance efficiency, Cost optimization, Sustainability. I also explicitly include Governance, Data management, Compliance, and Organizational readiness. Maximizing one dimension — like availability — without considering cost and operational capability produces an impractical design.
Hub-and-spoke vs mesh networking? Hub-and-spoke centralizes shared connectivity, inspection, DNS, egress, and hybrid links — simplifies governance, my default for most enterprises. Full mesh provides direct connectivity but becomes unmanageable at scale. At scale: AWS Transit Gateway, Azure Virtual WAN, or GCP Network Connectivity Center. Don't make the hub a throughput bottleneck.
How do you design a multi-tenant cloud platform? Identify isolation level: shared app+DB with tenant keys → separate schemas → separate DBs → separate namespaces → separate accounts. Propagate tenant identity through auth, authz, data access, caches, queues, logs, encryption, and billing. Include noisy-neighbor controls, tenant quotas, and automated onboarding/offboarding.
How do you optimize cloud cost? Establish ownership first (accounts, tags, budgets, showback). Then: rightsizing, autoscaling, commitment discounts for stable demand, spot/preemptible for tolerant workloads, storage lifecycle, managed service tier selection, data-transfer paths, log retention, non-prod scheduling. Assess cost per business transaction — not just the monthly bill.
Cloud bill doubles after moving to K8s — what do you examine? Overprovisioned node groups, inflated pod requests, low utilization due to scheduling constraints, unused LBs and public IPs, NAT Gateway egress charges, cross-zone traffic, excessive logging, orphaned volumes and snapshots, non-prod clusters running continuously, idle GPU nodes, missing autoscaler consolidation.
Solution & Enterprise Architecture
Architecture Decisions, API Design, Event-Driven & Trade-offs
What is the role of a Solution Architect? Converts business objectives into an implementable end-to-end design spanning application, data, integration, security, infrastructure, operations, cost, and delivery constraints. The role isn't just diagrams — it includes validating assumptions, resolving design risks, and ensuring delivered solutions meet both functional and non-functional requirements.
Functional vs non-functional requirements? Functional = what the system must do (search policies, generate invoices, enrol students). Non-functional = how well and under what constraints (availability, latency SLO, scale, security, auditability, recoverability). Many failed systems meet functional requirements but fail in production because NFRs weren't quantified or tested.
How do you make architecture decisions? Architecture Decision Records: context, decision drivers, considered options, selected option, rationale, consequences, assumptions, review date. Evaluate against business value, risk, security, reliability, cost, delivery time, operational skills, portability, and future change. Creates traceability — prevents reopening decisions without new evidence.
Synchronous vs asynchronous integration? Synchronous when caller needs immediate result and dependency can meet latency + availability. Asynchronous for long-running work, workload buffering, event distribution, loose coupling, and decoupled scaling. Anti-pattern: long chain of synchronous services where one slow dependency causes end-to-end outage. Async requires idempotency, DLQ, and correlation IDs.
How do you prevent microservices becoming a distributed monolith? Services around business capabilities with clear data ownership. Communicate through explicit contracts. Independent deployment, no shared databases, no chatty synchronous chains, no coordinated releases. Also recognize when a modular monolith is the right starting point — microservices aren't automatically better.
How do you measure whether architecture succeeded? Link to measurable indicators: availability and latency SLO attainment, deployment frequency, change failure rate, recovery time, security findings, cost per transaction/user, capacity behavior, user adoption. Architecture is successful when it enables business outcomes sustainably — not just when implementation matches the original diagram.
Scenario-Based
Live Debugging, Zone Failures, Capacity & Architecture Trade-offs
App has intermittent 503s — how do you investigate? Check: healthy target count on LB → ingress controller error logs → K8s Service endpoints → readiness probe failures → pod restarts/CPU throttling/OOM → HPA scaling delay → node pressure or zone imbalance → application connection pools → DB connection limits → timeout/keep-alive settings → NetworkPolicy errors. Intermittent 503s often = readiness instability or capacity saturation.
One availability zone fails — what should happen? Traffic routing stops sending to unhealthy targets. Replicas in other zones continue. Failed pods recreate where capacity allows. Autoscaler adds replacement capacity. Data tier stays available via multi-zone replication. Monitoring fires a meaningful alert. System operates with reduced redundancy — avoid risky changes until zone recovers. This must be tested; distributing nodes doesn't prove survival.
How would you expose a private EKS app to corporate users only? Options: internal NLB/ALB with VPC peering or Transit Gateway; AWS PrivateLink; VPN or Direct Connect with internal DNS; Cognito + OIDC with IP allowlisting; or Zscaler/Netskope ZTNA overlay. Choice depends on whether users are on-prem, remote, or cloud-native, and whether clientless browser access or full network connectivity is required.
Pods stuck in Pending state — how do you diagnose? Inspect pod events and scheduler messages. Typical causes: insufficient CPU/memory, node selector/affinity mismatch, untolerated taint, unbound PersistentVolume, zone restriction, GPU unavailable, PodDisruptionBudget constraints, max pod density or IP exhaustion, ResourceQuota violation. Fix the underlying constraint — don't force placement manually.
How do you balance ideal architecture and delivery deadlines? Identify which controls are mandatory (safety, compliance, data integrity, operability) vs which can be phased. Define a minimum viable architecture with a credible path to target state. Don't trade away critical security, backup, audit, or recoverability for a date. Document technical debt with owners and deadlines — controlled evolution, not perfection or reckless shortcuts.
Build vs buy decision framework? Compare: strategic differentiation, functional fit, integration, customization, data control, security, compliance, scalability, vendor viability, licensing, implementation effort, operational cost, and exit strategy. Buy commodity capability when a mature service fits. Build what differentiates the business or requires specialized control. Use total lifecycle cost and risk — not just initial price.
Full interview libraries — complete Q&A with detailed answers:
AI & GenAI Architecture Library Data Fusion · RAG · Agentic AI · Bedrock · Guardrails · 50+ Q&A Kubernetes & Cloud Architecture Library K8s HA · Ingress · Cloud · Solution Arch · Scenarios · 100+ Q&A
How It Works

From raw data & raw questions to grounded answers

Two flow strips explaining the modern enterprise data platform and the agentic AI / RAG stack — what each stage does, why it matters, and the technologies behind it. Architecture diagrams below show the full component view.
How a modern data architecture works
A hybrid lakehouse moves data from raw sources to business value through five stages — each governed, observable, and replayable. Click the architecture diagram below for the full component view.
1
Ingest from anywhere
Pull data from databases, SaaS APIs, IoT devices, file drops, and partner streams. Batch jobs run on schedule or on-event; streaming jobs ingest in seconds.
Glue · Lambda · NiFi · Kafka · Kinesis · MQTT
2
Land in S3 — Raw zone
Every record stored immutably in source format, partitioned by ingest date. The audit trail. Reprocessing always starts here, so nothing is ever truly lost.
S3 · Bronze layer · JSON / Parquet / Avro
3
Clean & standardize — Silver
Glue jobs validate, dedupe, conform schemas, and enforce data contracts. Bad records are routed to a DLQ — surfaced, never silently dropped.
Glue Spark · Schema Registry · DLQ
4
Curate for business — Gold
Build facts, dimensions, marts, and ML feature tables that data product teams own with SLAs on freshness and completeness.
Star schema · Feature tables · Owned datasets
5
Serve to humans & models
The same Curated layer feeds Redshift dashboards, Athena ad-hoc queries, embedded BI, and ML / AI workloads — one governed source of truth for both analytics and AI.
Redshift · Athena · QuickSight · SageMaker · Bedrock
Why it matters
Separating Raw → Silver → Gold lets the business move fast on the curated layer while data engineers still have the raw history to backfill and re-process. Governance — IAM, lineage, PII masking, and audit — is enforced across all three layers from a single Glue Data Catalog, so analytics and AI share the same trust model.
How Agentic AI & RAG actually work
Instead of one large model trying to do everything, multiple specialized agents collaborate — grounded by retrieval, bounded by guardrails, and observable end-to-end.
1
User asks a question
A request enters through web, mobile, Slack, Teams, or an internal API. Auth, tenant context, and PII / prompt-injection guardrails run before any LLM is called.
API Gateway · WAF · OIDC · Input Guardrails
2
Orchestrator plans the work
A lightweight planning agent classifies intent and decides which specialized agents to invoke, in what order. Step Functions / Lambda manage state, retries, and failures.
Step Functions · Lambda · SQS · Circuit breakers
3
Retrieve grounded context (RAG)
The Retrieval Agent embeds the query, searches a tenant-scoped vector database, and re-ranks the top chunks. The model gets your facts, not its imagination.
Embeddings · OpenSearch / Pinecone / FAISS · Re-rank
4
Specialized agents collaborate
Summarization compresses long context, Tool-Use calls SQL or APIs, Compliance checks policy and citations, then the Response Agent composes the final answer.
Bedrock / Azure OpenAI · Function calling · Multi-agent
5
Guard, log, and learn
Output guardrails check for PII or leakage, high-risk responses route to a human reviewer, and every step is logged for audit, eval, and cost tracking.
Output Guardrails · HITL · Audit · Eval Harness
Why it matters
Agentic AI is not one giant prompt — it's a graph of small, replaceable agents with clear contracts. RAG keeps answers grounded in your documents instead of the model's training data. And the governance layer — prompt registry, eval harness, drift monitoring, audit log — is what turns a clever demo into a production system that survives retry storms, partial failures, and real scale.
Architecture Designs

Real-world system architectures

Interactive architecture diagrams across all delivered projects — enterprise data fusion, GenAI RAG systems, LMS AI platform, defense DevSecOps, automotive simulation, edge AI, and cloud platforms. Click any component to explore its role and design decisions.
Real-world
Reference
AWS + ARM Virtual Hardware Edge AI · IoT MQTT · SageMaker · Lambda
ARM Virtual Hardware Graviton4 Prod QA Dev Local Development </> Developer GitHub Source Control Commit CI/CD Pipeline AWS CodePipeline Registry Docker Image Amazon ECR Dockerfile Serverless Compute λ IoT MQTT Protocol Database Amazon DynamoDB Output EC2 UI Application Records Model Training & Deployment Amazon SageMaker ml.c5.2xlarge Data Processing Monitoring CloudWatch Metrics+Alarms Notifications Amazon SNS Alerts/Email/SMS 📷 Camera AWS IoT Core ARM Virtual Hardware Crop Shots 📷 Camera AWS IoT Core ARM Virtual Hardware CPU / NPU STORAGE NETWORK AUDIO DISPLAY Crop Shots Storage S3 Object Store Code Crop Shots
CI/CD Pipeline
Edge / IoT
ML / SageMaker
Storage / DB
Monitoring / Alerts
Compute / EC2
Air-Gapped Infrastructure Gitea · Jenkins · Tuleap · Nexus · SonarQube · Trivy · Grafana
</> Developer Code Commit Unified DevOps Dashboard (UI Layer) SonarQube Grafana Mattermost Toolchain Component & Integration Layer REST API · Webhooks · Event Bus REST API Webhooks Version Control Gitea Self-hosted Git Project Mgmt & ALM Tuleap ALM · RBAC · LDAP · Audit Modeling & Design Modelio Data Flow Artifact Repository Nexus Repository Maven · Docker · npm · Helm Build & CI/CD Jenkins Declarative Pipelines · ARM64 Quality Control SonarQube Trivy LDRA Parasoft Communication & Collaboration Mattermost Monitoring & Analytics Grafana Prometheus Server 1: Core CI/CD & ALM Dashboard · Gitea · Tuleap · Jenkins · Nexus SonarQube · Trivy · OWASP ZAP · LDRA · Parasoft Server 2: Monitoring & Collaboration Grafana · Prometheus · Mattermost Modelio · Jitsi Meet (isolated from build load) Health Status Health Monitoring Monitoring Data Flow
Dashboard / Integration
Build / Quality
Artifact Store
ALM / Project Mgmt
Communication
Modeling
Enterprise LMS · Single-DB Multi-Tenant Kubernetes on AWS EKS · 1M+ Users · 76 Universities Zones: North · East · West · South Per-Tenant Domain · Logo · Logical Data Separation
Edge Security & Delivery Cloudflare / AWS WAF DDoS · Rate Limiting Bot Protection AWS CloudFront CDN Global edge caching Origin Shield SSL / TLS Custom domain HTTPS cert-manager auto-renew User Access Admin / Super Admins Instructors Trainees Kubernetes Application Layer NGINX Ingress Controller Load balancing · Routing Rate limiting · SSL offload Keycloak SSO SAML/OIDC · RBAC · 2FA LDAP sync · Audit logs API Gateway JWT validation · Rate limiting · mTLS · Schema validation LMS Core Courses + paths Single-DB MT Assessment MCQ + Grading Question banks Certification Auto-generated KMS signed PDF Analytics API Reports + Dashboard DORA · Engagement Jenkins CI/CD + Security Scans Trivy · SonarQube · Gates Notification Svc Email + SMS Alerts Video Streaming Svc DRM · HLS · Playback analytics Enterprise Integrations Zoho CRM SF · Learner sync HRMS / AD User provisioning Zoom / Teams Live sessions Gov't SSO Portal SAML federation REST API / SAML / SCIM DevSecOps & Infrastructure Kubernetes Cluster EKS · Workers Masters · ALB Monitoring Prometheus + Grafana Backup & DR Velero+S3 Data & Media Layer PostgreSQL Single shared DB Row-level (tenant_id) RLS · KMS encrypted Redis Cache Session · Queue Cluster mode 3 shards · TTL Object Storage Content · SCORM Pre-signed URLs Glacier tiering Enterprise Media Platform Video · DRM HLS · Delivery MediaConvert Tenant Layer — 76 Universities · Single-DB Multi-Tenant · Per-Tenant Domain + Logo NORTH 20 Universities Delhi · Punjab · Haryana UP · Uttarakhand · HP · J&K own domain · own logo logical separation (tenant_id) EAST 18 Universities West Bengal · Bihar Jharkhand · Odisha · Assam · NE own domain · own logo logical separation (tenant_id) WEST 19 Universities Maharashtra · Gujarat Rajasthan · MP · Goa own domain · own logo logical separation (tenant_id) SOUTH 19 Universities Karnataka · Tamil Nadu Kerala · Andhra · Telangana own domain · own logo logical separation (tenant_id)
Edge Security / CDN
Kubernetes Layer
Data / Media
Tenants (76 Universities · N/E/W/S)
Users / DevSecOps
Generic Reference AWS · Multi-AZ · VPC · EKS · RDS · S3 Route 53 · CloudFront · ALB · Lambda · SageMaker
Edge & DNS Route 53 (DNS) CloudFront CDN WAF + Shield API Gateway VPC (Multi-AZ) Public Subnet Application LB NAT Gateway SSM / Bastion Transit Gateway Private Subnet — Compute EKS Cluster Managed K8s HPA · Cluster AS EC2 Auto Scaling Spot + On-Demand Launch templates Lambda (Serverless) Event-driven Provisioned concurrency Fargate Serverless containers ECS Container orchestration SageMaker ML Training · Inference Private Subnet — Data RDS Aurora (Multi-AZ) Primary + Read replicas DynamoDB NoSQL · Global ElastiCache Redis · Memcached OpenSearch (Logs + Search) Storage · Registry · Messaging S3 Object Storage Lifecycle · Versioning Replication · KMS ECR Container registry Image scanning SQS + SNS Queues · Pub/Sub FIFO · DLQ EventBridge Event bus Schema registry Kinesis Streams + Firehose Real-time ingestion S3 · OpenSearch delivery Observability · Security · IAM CloudWatch Metrics · Logs Alarms · Insights X-Ray Distributed tracing GuardDuty Threat detection VPC + CloudTrail Security Hub + AWS Config Compliance posture IAM + KMS Roles · Policies Key rotation CloudTrail Audit log Compliance
Edge / DNS
Network / Storage
Compute
Data Layer
Observability / Security
Generic Reference Field → Edge → Cloud → Analytics ARM · TFLite · MQTT · IoT Core · Kinesis · SageMaker
1 · Field Layer ARM Cameras RGB / IR · 30fps On-device inference Cortex-A + NPU MCU Sensors ESP32 · STM32 · nRF52 Temp · Humidity · GPS LoRa · BLE · Battery Actuators Relays · Motors · Pumps Fail-safe defaults Connectivity 4G/5G · LoRa · Wi-Fi Multi-link failover 2 · Edge Layer Edge Gateway Jetson · Pi CM4 · i.MX8 Aggregation · orchestration Local storage buffer TFLite / ONNX YOLO · MobileNet INT8 quantized NPU / GPU accelerated Mosquitto MQTT Local pub/sub broker Persistent queues Greengrass / SiteWise Local Lambda runtime Offline buffering Stream processing 3 · Cloud Layer AWS IoT Core MQTT broker · X.509 auth Device registry Rules engine Kinesis Streams Real-time ingestion Partition by device_id Lambda Processor Anomaly detection Enrichment · aggregation S3 Data Lake Parquet · Partitioned Glacier tiering DynamoDB / DocDB Device state Hot-path reads Device Management OTA firmware Device shadow 4 · Analytics Layer SageMaker Training Batch training jobs Model registry Distributed GPU Model Deployment OTA to edge · Endpoints A/B · Canary rollout MongoDB / TimeSeries Long-term storage Aggregation pipelines Grafana + QuickSight Real-time + BI Ops + Business views Alerting PagerDuty · Slack · SMS Escalation policies API / Web + Mobile REST · GraphQL WebSocket live state
Field
Edge
Cloud
Analytics
Generic Reference Gitea · Jenkins · SonarQube · Trivy · Nexus · Ansible LDAP · Vault · EFK · Prometheus · PagerDuty
CI/CD Pipeline Developer Code · Commit Pre-commit hooks Gitea / GitLab Merge requests Protected branches Jenkins Jenkinsfile · Stages Parallel builds SonarQube SAST · Code quality Quality gate Trivy CVE scanner Images · IaC · Secrets Testing Unit · Integration Coverage gate Nexus Repo Docker · Maven · npm Retention policies Tuleap ALM Approval gate · CAB Audit log Ansible / TF IaC · Playbooks Remote state DEV → UAT → PROD Blue/Green · Canary rollouts Auto rollback < 5 min Supporting Systems LDAP / SSO OpenLDAP / FreeIPA SAML / OIDC Group-based RBAC Vault Secrets · Certs Dynamic · TTL Pipeline injection EFK Stack Elastic + Fluentd + Kibana Centralized logs Prometheus + Grafana DORA metrics Pipeline metrics PagerDuty On-call · Escalation Runbook links Incident response SIEM Splunk / Wazuh Audit · Policy alerts SOC2 / ISO 27001 SBOM · Signing CycloneDX / SPDX Sigstore · in-toto SOC2 / ISO evidence Chaos Engineering Litmus · Chaos Mesh Fault injection Quarterly game-days Policy-as-Code OPA / Conftest IaC policy gates Automated reviews DAST · Pen Testing OWASP ZAP · Burp External pen tests Severity-based SLAs
Developer
Pipeline Core
Security / Quality
Identity / Ops
Incident / SIEM
Generic Reference Control Plane · Worker Nodes · Istio · ArgoCD Prometheus · Grafana · Loki · Jaeger · OPA
Control Plane API Server REST gateway TLS · OIDC etcd Cluster state · Raft 30-min snapshots Scheduler Pod placement Affinity · Taints Controller Mgr Reconciliation Built-in controllers HPA · VPA · KEDA Autoscaling Event-driven Velero Backup · DR PV snapshots Worker Nodes · Namespaces · Service Mesh Istio (Mesh) Envoy sidecars mTLS · Traffic shift Circuit breaking OPA Gatekeeper Admission control Policy-as-Code Image whitelist Calico / Cilium CNI + Policies eBPF observability Egress control ArgoCD GitOps Git = source of truth Auto sync + heal App-of-apps cert-manager + External Secrets Let's Encrypt · Vault CA Auto-renewal Sync secrets from Vault/AWS SM Namespaces (team-a · team-b · shared · infra) ResourceQuotas · LimitRanges · NetworkPolicies RBAC RoleBindings · PodSecurityStandards Persistent Volumes CSI drivers · StorageClasses Snapshots · Clones Ingress Controller NGINX / Traefik · TLS External-DNS · WAF Observability Stack Prometheus Metrics · Alertmanager Thanos long-term Grafana Unified dashboards SLO / Error budgets Loki Log aggregation S3 backend Jaeger / Tempo Distributed tracing OpenTelemetry Falco Runtime security eBPF · SIEM alerts Kiali Mesh topology mTLS status Infrastructure Foundation Nutanix HCI · VMware · Bare Metal · EKS / GKE / AKS (hybrid-ready)
Control Plane
Mesh / GitOps
Namespaces / Ingress
Observability
Infrastructure
Generic Reference CARLA · ROS2 · ARM Virtual Hardware · HIL EKS · SageMaker · ISO 26262
Simulation Stack CARLA Simulator Urban · Weather · Traffic OpenDRIVE maps Python API · Scenario scripting ROS2 DDS pub/sub Sensor + control messages Real-time capable ARM Virtual Hardware Cycle-accurate SoC Actual ECU firmware Parallel cloud runs Scenario Library Test cases · Edge cases Parameterized fuzzing Real-world log replay Sensor Models — LiDAR · Camera · Radar · IMU · GPS Realistic noise models · Failure injection · Hardware-calibrated Compute Orchestration GPU Cluster A100 / H100 MIG partitioning Parallel sim + training EKS Orchestrator K8s Jobs + Argo WF Parallel runs Priority queues HIL Test Bench Hardware-in-Loop Real ECUs · Virtual sensors Bridge virtual + physical Spot Pool Preemptible GPU Auto-resume 70% cost reduction Data Pipeline — S3 · Parquet · Partitioned Drive logs · Simulation artifacts · Sensor recordings Safety & Validation ISO 26262 Safety ASIL A-D classification Hazard to req to test trace SOTIF (ISO 21448) AI / sensor limits Edge-case coverage Automated Safety Validator (block-on-fail) Collision rate · TTC · Lane-keep · Pedestrian detection SLAs Continuous ML Training SageMaker Training Perception models Distributed · Nightly Model Registry Versioned · Lineage Shadow-mode OTA Fleet Deployment Beta to 1% to 10% to 100% · Telemetry-based auto-rollback Feedback Loop — Fleet Telemetry to Drive Log Replay to New Scenarios Disengagements · Near-misses · Sensor anomalies to Scenario library growth (continuous improvement)
Simulation
Compute
Safety
ML Pipeline
Fleet Feedback
Data Bridge Platform · Real-World S3 Medallion · Glue PySpark · Textract · OpenSearch · Bedrock RAG Finance & Staffing Intelligence · 5+ Systems Unified
L1 SOURCES L2–3 INGEST L4 S3 LAKE L5 ELT L6 RAG L7–8 Layer 1 — Source Systems Amazon RDS PostgreSQL · MySQL · CRM S3 Landing Zone PDF Invoices · POs · Contracts Approval Emails · Excel Semi-structured sources Timesheet · Payroll CDC via AWS DMS Approval Portals · Vendor APIs Lambda scheduled pull Layer 2 — Ingestion (Glue + EventBridge + DMS) & Layer 3 — Document Processing (Textract) AWS Glue ETL + Bookmarks Incremental · Watermark · Crawlers Job Bookmarks → No Duplicates AWS DMS · CDC Row-level change capture Only changed rows propagate Amazon Textract (Async) Invoice fields · PO numbers · Dates SNS completion → result Lambda Amazon EventBridge S3 events · Glue triggers Central event-driven trigger AWS Lambda S3 trigger → Textract DynamoDB confidence log Layer 4 — S3 Medallion Storage Bronze · Raw Layer KMS encrypted · S3 Object Lock · Full fidelity · Append-only Silver · Processed Layer Cleaned Parquet · Deduped · Schema-conformed · Athena-queryable Gold · Curated Layer Invoice · Project · Employee · PO · Timesheet KPIs · Delta Lake Layer 5 — Transformation (AWS Glue PySpark ELT) Entity Resolution (PySpark) Deterministic keys · Cross-system dedup Glue Data Quality Completeness · Uniqueness · Referential integrity KPI Calculation Billing margin · Approval lag · PO balance Amazon Redshift Star schema · Spectrum · SPICE feeds Layer 6 — Intelligence: OpenSearch Hybrid RAG + Amazon Bedrock Titan Embeddings v2 1536-dim · Same model ingest + query Semantic alignment guaranteed OpenSearch · BM25 + HNSW k-NN Hybrid retrieval · Semantic reranker Security trim DURING query (role-scoped) Bedrock Claude 3 Sonnet Grounded answers · Invoice/PO citations Bedrock Guardrails · Prompt injection screen QuickSight SPICE · Row-level security IAM RBAC dashboards Layer 7–8 — Security & Governance IAM Identity Center AWS Lake Formation VPC PrivateLink KMS CMK per Tenant CloudTrail + CloudWatch Secrets Manager
Source Systems
Ingestion / ELT
Textract
S3 Medallion (Bronze/Silver/Gold)
RAG / Bedrock
Redshift / QuickSight
Security & Governance
HR Policy RAG Assistant · Real-World Amazon Bedrock · Titan Embeddings v2 · OpenSearch · Textract Ingestion Pipeline + Query Pipeline · Security Trimming · Temperature 0.1
INGESTION PIPELINE QUERY PIPELINE ① HR Document Upload (S3 Versioned) S3 PUT → EventBridge → Lambda · Every version retained · DynamoDB audit log PDF policies · SOPs · Leave rules · Benefits guides ② Amazon Textract (Async + SNS) Text · Tables · Section headings · Key-value pairs Confidence scores → DynamoDB · Native PDFs + scanned ③ Clean & Structure-Aware Chunk Section boundary splits · 300–500 tokens · 50–80 overlap Rule never separated from its exception ④ Metadata Enrichment policy_name · section · effective_date · access_classification · dept access_classification = security trimming key ⑤ Titan Embeddings v2 → OpenSearch Index 1536-dim vectors · Same model as query (alignment critical) HNSW k-NN + BM25 keyword fields indexed in parallel DynamoDB version control · Old versions flagged inactive ① Authenticate — IAM Identity Center (SSO) JWT: user_id · access_level · department · group memberships API Gateway validates JWT on every request ② Normalise → ③ Embed (Titan Embeddings v2) Abbreviation expansion · Pronoun resolution · 1536-dim query vector Identical semantic space to document index ④ Hybrid Retrieval — OpenSearch BM25 + HNSW k-NN BM25 exact terms + semantic k-NN simultaneously Semantic reranker reorders top-20 by true intent ⑤ Security Trimming — Field-Level Security DURING Retrieval Unauthorised chunks NEVER enter LLM context · OpenSearch FLS Not post-retrieval filtering — applied at query time ⑥ Threshold Check → ⑦ Bedrock Claude 3 Sonnet Temperature 0.1 · Citation required per factual claim Relevance threshold fallback → no hallucination on weak evidence CloudWatch: full interaction log with correlation ID Target: <5% Hallucination Ground-truth eval sets Temperature 0.1 Factual · Consistent · Cited 1536-dim Vectors Titan Embeddings v2 · Both sides Security Trim @ Query Never post-retrieval Version Audit Effective date retrievable Shared Index
Ingestion Pipeline
Textract · Normalise
Chunking · Retrieval
Embeddings · Bedrock
Auth / IAM
Security Trimming
LMS AI Content Generation · Real-World 1M+ Learners · 76 Universities · Bedrock · Polly · Transcribe · SageMaker · Rekognition 7 AI Microservices · Step Functions · AIMS/ISO 42001 · Human-in-Loop
Layer 1 — Existing LMS Core (Unchanged) · Multi-Tenant Kubernetes on Nutanix HCI Moodle LMS Core Keycloak JWT · Tenant Context 76 Universities · 1M+ Learners · Multi-Tenant REST APIs · Course / Progress CI/CD · AIMS/ISO 42001 Layer 2 — AI Service Mesh (ai-services namespace) · 7 Independent Microservices · Each HPA-scaled Content Gen Orchestrator → Bedrock Claude 3 Audio Synthesis Polly Neural 8 Indian langs MP3 · SSML Video Transcription Transcribe Step Functions WebVTT · Diarize Translation AWS Translate Custom Terminology 8 regional langs Recommendation SageMaker Shadow Variants A/B · Clarify Video Moderation Rekognition 80% threshold Pre-publication RAG Gateway Bedrock KB OpenSearch Serverless Layer 3 — Step Functions Video Enrichment Pipeline ① S3 UploadEventBridge → SF start ② RekognitionContent moderation ③ TranscribeTimestamped + diarize ④ WebVTT CaptionsLambda → LMS REST ⑤ Chunk + MetadataTimestamp_range tag ⑥ Embed + IndexBedrock KB · OpenSearch ⑦ SNSFaculty notify Layer 4 — Async Backbone + S3 Multi-Zone Storage SQS · Human-in-LoopFaculty review queue · FIFO · DLQ S3 Multi-Zone · CloudFront CDNlms-raw · lms-processed · lms-curated · lms-audio (lifecycle) SNS · Faculty NotificationsCompletion · Moderation flags · Budget alerts EventBridge · S3 → Step FunctionsUpload events · AI pipeline triggers Layer 5 — Observability + Governance + Security CloudWatch + X-Ray + Grafanatenant_id + course_id dimensions · Per-tenant budgets Bedrock GuardrailsPrompt injection · PII output screening IAM Tenant Roles · KMS CMKs · PrivateLinkPer-tenant encryption · VPC PrivateLink (no public internet) AIMS/ISO 42001 Audit TrailModel version · Cost · Reviewer action · Correlation ID 1M+ Learners Active on platform 76 Universities Namespace-isolated 40–60% Authoring ↓ Content generation reduction 8 Indian Languages Polly Neural TTS 95% Caption Coverage WebVTT · Transcribe Human-in-Loop All AI content reviewed
LMS Core / Storage / Async
AI Service Mesh (7 microservices)
Step Functions Video Pipeline
Observability / Governance / AIMS
Bedrock / Titan / OpenSearch
Awards & Recognition

Credibility earned through delivery

Recognition earned through enterprise delivery, technical ownership, and contribution to impactful programs and innovation showcases.
2025
Edge AI & Distributed Systems Speaker
AI Summit & Embedded World
Demonstrated hybrid deterministic edge platforms to international audiences at Nuremberg and AI summit stages.
2024
Star Performer of the Year
Visionext / Judge India Solutions
Recognized for major contribution to the Visionext project and delivery excellence in high-impact AWS edge-cloud infrastructure.
2024
Cloud Architecture Leadership Award
Visionext
Awarded for architecting scalable AWS edge-cloud infrastructure on the Visionext platform.
2023
Kubernetes Infrastructure Excellence – ACE Award
ICAR
Acknowledged for strong contribution to platform and infrastructure success supporting 76 universities nationwide.
2012
Enterprise Support Excellence
Dell Perot Systems
Recognized for consistently accelerating SLA resolution and improving operational efficiency in global enterprise support.
"The strongest solutions are not only architected well — they are built to survive real environments, real scale, and real operational pressure."
This approach defines the professional style: blending architecture thinking, infrastructure discipline, security mindset, cloud fluency, and emerging technology experimentation into one practical delivery model.
Prashant Yadav · Senior Solutions Architect
"Modern enterprise platforms aren't built in silos — application architecture, data architecture, and AI orchestration only deliver real value when they're connected and governed as one system."
From hybrid lakehouses and multi-agent RAG platforms to Kubernetes serving 1M+ users — the discipline is the same: engineer for clarity, govern for trust, operate for resilience.
On building integrated platforms · Architecture philosophy
Technology Leadership
Speaker & Demonstrator at Embedded World 2025, Nuremberg, Germany.
Speaker at AI Summit — presented Distributed Intelligence Mesh and hybrid edge architectures.
Edge AI research using ARM platforms, TensorFlow Lite, and MPU+MCU deterministic systems.
Architecture PoCs for distributed edge intelligence and hybrid cloud systems.
Technology evangelism across DevOps, edge computing, and cloud-native architectures.
Education

Academic foundation

Formal education underpinning technical expertise and strategic leadership capabilities.
PGE Master of Business Administration – Business Analytics
IMT-CDL Ghaziabad, Noida
2021 – 2022
Bachelor of Technology – Information & Technology
Dr. A.P.J. Abdul Kalam Technical University (IEC CET, Greater Noida)
2002 – 2006
Core Competencies & Soft Skills
Leadership & Soft Skills
LeadershipStrategic ThinkingPublic Speaking Cross-Functional CollaborationStakeholder Communication Decision-MakingInnovationProblem Solving
Business Competencies
Cloud Cost OptimizationVendor Management Budget ReportingRFP Participation POC DevelopmentIT Governance Technology StrategyArchitecture Design
Connect

Let's build something meaningful

Available for solution architecture, cloud modernization, Kubernetes platform engineering, DevSecOps transformation, edge AI initiatives, and enterprise technology consulting.

Email
prashuyadav@outlook.com
LinkedIn
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Current Focus
Data Architecture · Agentic AI & GenAI · Hybrid Cloud · Kubernetes · Edge AI
Role
Sr. Solutions Architect / Tech Leader
Location
Noida, UP, India
Languages
English · Hindi
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