AI Architect

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 & CloudNative 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.
  

MLOps / LLMOps

• Establish ML and GenAI CI/CD pipelines, including: 
• Versioned prompts, models, datasets
• Evaluation pipelines (quality, safety, cost)
• Monitoring, drift detection, approval, rollback
• Implement feature engineering and feature management using SageMaker AI (integrated feature store capabilities).
• Support A/B testing and canary deployment of GenAI solutions.

Security, Governance & Responsible AI

• Define endtoend security architecture for GenAI: 
• IAM, KMS, secrets management
• Data privacy, PII handling, prompt logging
• Enforce Responsible AI controls: guardrails, moderation, auditability, traceability.
• Align solutions with enterprise governance and compliance standards.
  

Reliability, Cost & Performance

• Define NFRs, SLIs/SLOs for GenAI (latency, throughput, cost per request).
• Implement observability using CloudWatch, XRay, OpenTelemetry.
• Optimize inference cost using caching, batching, routing, and model selection policies.

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.

Core Skills & Qualifications (Refined)

AWS Core

Musthave

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