FDE - Agentic Ai

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

•     Embed with customer engineering teams: Understand
the customer's 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.

Requirements

• 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
  

Benefits