JD For AWS AI Engineer

Job highlights

• Strong hands-on LangGraph / LangChain and proven production experience with Agentic AI and multi-agent systems
• POC,tutorial,certification or hackathon-only experience is not sufficient
• Experience : 5+ years . What You ll Do

Job Summary

JD for AWS AI Engineer

Job Details

• Position Title : AWS DevOps Engineer

• Job Location : Bangaluru

• Experience : 5+ years

  What You ll Do

• We re looking for a hands-on Senior AI Engineer with a strong software engineering background to build, deploy and operate production-grade Agentic AI and multi-agent solutions on AWS . You ll work from established designs, freely explore and experiment with new AI ideas, rapidly prototype/POC, and turn successful approaches into secure, scalable production solutions.

• This is an engineering and delivery role, not an architecture role.

• Build and deploy multi-agent solutions using Python , LangGraph and LangChain ; rapidly prototype/POC, and turn successful approaches into secure, scalable production solutions.

• Implement orchestration, tool calling, state/memory, checkpointing and Human-in-the-Loop (HITL) ; build and integrate MCP and A2A capabilities.

• Develop RAG / LLM solutions integrated with enterprise APIs, data and business systems.

• Build and operate on AWS using Amazon Bedrock and Amazon Bedrock AgentCore . Develop production APIs and integration services.

• Implement automated testing, CI/CD , AI evaluation, guardrails, observability and security controls including IAM, least privilege, secrets management and secure tool/API access.

• Troubleshoot and optimize live solutions for reliability, performance, latency and cost.

  What You ll Bring

• 5+ years of software engineering, AI/ML engineering or related development experience, with strong Python software engineering skills.

• Strong hands-on LangGraph / LangChain and proven production experience with Agentic AI and multi-agent systems.

• Strong hands-on AWS , Amazon Bedrock and Amazon Bedrock AgentCore , plus MCP and A2A implementation experience.

• Strong knowledge of RAG , LLMs , embeddings, vector search, tool calling, state/memory and HITL .

• Experience with APIs/ FastAPI , Docker, Git, CI/CD , automated testing, AI evaluation, guardrails, observability and production debugging.

• Ability to quickly understand established designs and independently deliver production-quality solutions at pace.

  Production Experience - Essential

Must have personally built, deployed and supported at least two production-grade multi-agent AI solutions. POC, tutorial, certification or hackathon-only experience is not sufficient.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.