Azure , AIML , Gen AI/ Agentic AI Architect

Job description

TCS Hiring !!!! Azure , AIML , Gen AI/ Agentic AI Architect **Role:** Experience Range: 10 to 12yrs NOTE: If the skills/profile matches and interested, please reply to this email by attaching your latest updated CV and with below few details: Name: Contact Number: Email ID: Highest Qualification in: (Eg. B.Tech/B.E./M.Tech/MCA/M.Sc./MS/BCA/B.Sc./Etc.) Current Organization Name: **Total IT Experience-10 - 12** LOCATION TCS -Chennai Bangalore, pune Hyderabad Current CTC Expected CTC Notice period: Whether worked with TCS - Y/N **JD: AWS Architect AI/ML & Generative AI** **Role Overview** The AWS Architect – AI/ML & Generative AI is responsible for defining, designing, and governing enterprisescale GenAI, AI/ML, and cloudnative architectures on AWS. The role focuses on building secure, scalable, and productionready GenAI solutions using Amazon Bedrock and SageMaker AI, modernizing legacy platforms into eventdriven and serverless architectures, and establishing LLMOps/MLOps standards. This architect acts as a technical leader and advisor, working closely with product, engineering, data, and security teams to deliver businessaligned, costoptimized, and responsible AI solutions. **Key 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 - Embedding strategies, chunking, metadata enrichment, reranking, caching, and freshness controls - Implement hallucination mitigation techniques, citation grounding, and response evaluation. **Modernization &** **Cloud****Native** **Architecture** - Modernize legacy workloads into microservices, eventdriven, and serverless architectures using: - EKS / ECS / Lambda - API Gateway, EventBridge, SQS, SNS - Apply domaindriven design (DDD) and integration best practices. - Design and expose APIs using REST and GraphQL patterns (including schema design, resolver performance, and backend integration) based on usecase requirements.