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.
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.
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.
Python (Automation)React.jsChatGPT · Claude · CopilotVendor 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.
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 Architecture & Analytics Platforms
Designed enterprise data lakes on S3 with Raw / Processed / Curated layering, Redshift warehouses, and PostgreSQL reporting layers feeding dashboards and AI/ML datasets.
Built ETL/ELT frameworks using AWS Glue (Crawlers, Jobs, Data Catalog), Lambda, NiFi, and Python — orchestrated end-to-end on Amazon MWAA (Airflow).
Implemented streaming pipelines (Kafka, Kinesis, MQTT, IoT Core) and governance via RBAC, IAM, metadata catalog, lineage, audit logging, PII masking, and KMS encryption.
Agentic AI, GenAI & RAG Systems
Architected multi-agent AI platforms on AWS Bedrock and Azure OpenAI — orchestrator, classification, retrieval, summarization, compliance, and response agents collaborating via event-driven workflows.
Built RAG pipelines with chunking, embeddings, and vector databases (OpenSearch, Pinecone, FAISS) for grounded, context-aware enterprise responses.
Implemented enterprise AI governance — PII masking, tenant isolation, prompt guardrails, model versioning, drift monitoring, audit logging, and human-in-loop validation.
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.
Enterprise Data Platform
Hybrid Lakehouse · Batch + Streaming
Architected an end-to-end enterprise data platform on AWS — S3 data lake (Raw / Processed / Curated), Redshift warehouse, and PostgreSQL reporting layer feeding dashboards and AI/ML datasets. Built ETL/ELT with AWS Glue, Lambda, NiFi, Python, orchestrated on Amazon MWAA (Airflow). Streaming via Kafka, Kinesis, MQTT, IoT Core. Governance with RBAC, IAM, metadata catalog, lineage, audit logging, PII masking, and KMS encryption.
S3 Data LakeRedshiftAWS GlueMWAA / AirflowKafka · KinesisLineage · RBAC
Enterprise GenAI
Agentic AI & RAG Platform
Designed a multi-agent agentic AI platform on AWS Bedrock and Azure OpenAI — orchestrator, classification, retrieval, summarization, compliance, and response agents collaborating via event-driven workflows. Built RAG pipelines (chunking → embeddings → vector DB → retrieval → LLM) over OpenSearch / Pinecone / FAISS. Implemented PII masking, tenant isolation, prompt guardrails, model versioning, drift monitoring, and human-in-loop validation. Solved retry storms and queue buildup with circuit breakers, DLQs, retry backoff, and graceful degradation.
Architected AWS-based vSoC simulation platform enabling scalable automotive validation environments using ARM Graviton and GPU workloads. Implemented Docker, Kubernetes and CI/CD with secure VPC isolation reducing provisioning time by 70%.
AWSARM GravitonKubernetesCI/CD
Siemens / CES 2026
ARM CSS Automotive Migration
Led migration of automotive workloads to ARM CSS architecture improving CARLA simulation performance. Enabled embedded and cloud convergence validation for automotive demonstration environments.
ARM CSSCARLACloud 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.
TuleapJenkinsGiteaRBACLDAP
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.
KubernetesNutanix HCI1M+ Users76 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 CoreGreengrassSageMakerMQTTMongoDB
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 LiteMPU+MCUMQTTEdge 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 OrchestrationSBCMicro 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 GreengrassLambdaEdge 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 SnapshotsS3 BackupFailover
Enterprise
O365 & Azure Migration
Migrated Exchange, File Server and Active Directory to Office 365 and Azure modernizing identity architecture for enterprise workforce continuity.
Office 365Azure ADIdentity
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.
DockerCI/CDProduction
UKSC
Cloud-Native LMS Deployment
Deployed Kubernetes-based LMS platform with automated DEV-UAT-PROD pipelines improving release stability and reducing manual deployment overhead.
KubernetesDEV/UAT/PRODPipelines
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.
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.
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 & generic system architectures
Ten interactive architectures — the real ones I've delivered across enterprise, defense, automotive, and AI projects, plus reference designs for cloud, edge AI, DevSecOps, Kubernetes, data platforms, and agentic AI / GenAI. Click any component in any diagram to explore its role and design decisions.
Real-world
Generic
AWS + ARM Virtual HardwareEdge AI · IoT MQTT · SageMaker · Lambda
Enterprise LMS · Single-DB Multi-TenantKubernetes on AWS EKS · 1M+ Users · 76 UniversitiesZones: North · East · West · SouthPer-Tenant Domain · Logo · Logical Data Separation
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.
"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.
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)
Available for solution architecture, cloud modernization, Kubernetes platform engineering, DevSecOps transformation, edge AI initiatives, and enterprise technology consulting.
