AWS AI Architect

Key Responsibilities

• Design and implement AI/ML solutions on AWS for enterprise-scale applications.
• Build and deploy Generative AI, LLM, RAG, and AI Agent solutions.
• Architect AI platforms using Amazon Bedrock, SageMaker, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, and OpenSearch.
• Develop scalable Python-based AI/ML services, REST APIs, and microservices.
• Implement RAG pipelines using embeddings, vector databases, document processing, and semantic search.
• Build AI agents and agentic workflows with tool calling, orchestration, planning, and autonomous decision-making.
• Integrate LLMs with enterprise applications, databases, APIs, and business workflows.
• Design MLOps/LLMOps pipelines for model training, evaluation, deployment, monitoring, and governance.
• Implement AWS security using IAM, KMS, Secrets Manager, VPC, CloudTrail, GuardDuty, and related services.
• Optimize AI workloads for performance, scalability, reliability, and cost.
• Lead technical discussions with architects, engineering teams, product teams, and business stakeholders.
• Mentor senior engineers and establish AI engineering best practices.

Required Skills

• 10+ years of software engineering / AI / ML experience.
• Strong Python development experience.
• Hands-on AWS experience with:
  • Amazon Bedrock
  • Amazon SageMaker
  • AWS Lambda
  • ECS / EKS
  • S3
  • DynamoDB
  • OpenSearch
  • API Gateway
  • CloudWatch
  • IAM
• Strong experience with Generative AI and LLMs.
• Experience with RAG architecture, embeddings, vector databases, prompt engineering, and LLM evaluation.
• Experience with LangChain, LlamaIndex, or similar AI frameworks.
• Knowledge of OpenAI, Anthropic Claude, Llama, or other foundation models.
• Experience developing REST APIs and microservices.
• Strong knowledge of Docker, Kubernetes, CI/CD, Git, and Infrastructure as Code.
• Experience with ML pipelines, MLOps, model monitoring, and production deployment.
• Strong understanding of AWS architecture and cloud security.