FDE Agentic Ai

Qualification: BTech/MTech/MCA
Mode of Work: Hybrid

Embed with customer engineering teams: Understand the customers business processes technical architecture and operational constraints. Turn ambiguous business problems into scalable AI-enabled production systems.

Build production AI applications: Design and develop production-grade software for AI and agentic workflows including multi-agent orchestration retrieval pipelines workflow automation and decision intelligence. Integrate foundation models customer data sources APIs and existing applications into cohesive AI experiences. Optimize for latency reliability observability cost and security.

Own production: Take systems from design through production rollout and ongoing operations. Troubleshoot incidents across AI models distributed systems data pipelines and application services. Put in place monitoring evaluations guardrails testing CI/CD rollback and resiliency mechanisms that keep AI systems healthy at scale.

Accelerate customer transformation: Identify opportunities to expand AI adoption across customer workflows. Build reusable patterns accelerators and reference architectures for future engagements.

Raise the bar: Mentor engineers and contribute to engineering best practices for AI and forward deployment. Feed reusable components and learnings back into products and services.

• Python: Strong production-grade Python clean code testing APIs and integration with enterprise systems.
  
• Agentic AI on AWS: Hands-on experience building multi-agent systems on Amazon Bedrock and Bedrock AgentCore including:
  
• Prompt engineering
  
• Tool calling
  
• Agent orchestration with a framework (e.g. Strands Agents LangGraph LangChain)
  
• RAG & Retrieval: Building retrieval pipelines including:
  
• Embeddings
  
• Chunking
  
• Vector search (e.g. Bedrock Knowledge Bases OpenSearch pgvector)
  
• Core AWS Services: Working knowledge of:
  
• Serverless and container services (e.g. Lambda ECS/EKS)
  
• API Gateway
  
• Storage (e.g. S3 DynamoDB)
  
• Security and networking (e.g. IAM)
  
• Production Engineering: Experience taking AI systems to production including:
  
• CI/CD
  
• Automated testing
  
• Monitoring
  
• Evaluations and guardrails for AI output