Role & responsibilities

GenAI Architecture
Design and deliver Generative AI and agentic architectures on AWS using Amazon Bedrock and SageMaker AI, including prompt engineering, guardrails, tool/function calling, memory, evaluation, and multiagent patterns.
Define model selection strategies (foundation vs finetuned vs hosted OSS models) with latency, cost, and quality tradeoffs.
Establish GenAI reference architectures for enterprise adoption.

RAG & Knowledge Systems
Build RetrievalAugmented Generation (RAG) systems using:
Vector stores: Amazon OpenSearch (vector engine), Aurora PostgreSQL (pgvector), DynamoDB (vector search), Pinecone

MLOps / LLMOps
Monitoring, drift detection, approval, rollback
Implement feature engineering and feature management using SageMaker AI (integrated feature store capabilities).

Security, Governance & Responsible AI
Define endtoend security architecture for GenAI:
IAM, KMS,



secrets management
Enforce Responsible AI controls: guardrails, moderation, auditability, traceability.

Stakeholder Engagement & Enablement
Lead architecture reviews and design forums.
Mentor engineers and architects across teams.
Support solutioning, estimation, PoCs, and RFP responses for GenAI initiatives.

Preferred candidate profile Core Skills & Qualifications (Refined)

AWS Core

Musthave
VPC, IAM, KMS, CloudWatch, CloudTrail
S3, DynamoDB, RDS/Aurora
EKS / ECS / Lambda
API Gateway, EventBridge, SQS, SNS

GenAI & AI/ML

Musthave
Amazon Bedrock (models, guardrails, evaluation)
SageMaker AI (training, hosting, pipelines)
Prompt engineering, RAG, embeddings, evaluation

Cost/latency optimization strategies

📌 Aws , Aiml , Gen Ai/ Agentic Ai Architect Bengaluru (India)
🏢 Tata Consultancy Services
📍 India