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)
🏢 Tata Consultancy Services
📍 Bengaluru