AWS , AIML , Gen AI/ Agentic AI Architect

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