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Role at a glance
You will
Build and deploy multi-agent solutions using Python, LangGraph and LangChain;
rapidly Prototype/POC and productions successful approaches.
Implement orchestration, tool calling, state/memory, checkpointing and Human-in-the
Must-have skills
AWSCI/CDDockerFastAPIGitProduction Engineering
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Job description
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. What You'll Do Build and deploy multi-agent solutions using Python, LangGraph and LangChain; rapidly Prototype/POC and productions successful approaches. 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. Team & Culture Hands-on engineering, experimentation, knowledge sharing and continuous learning. You'll have the freedom to explore new Agentic AI technologies and ideas, share reusable patterns and help shape AI initiatives while maintaining strong security and production engineering standards. Good to Have Broader AWS services Terraform/CloudFormation Kubernetes/EKS OpenTelemetry/LangSmith SageMaker/MLflow Azure/GCP exposure Experience working in SAP environments or integrating AI solutions with SAP is a bonus. Preferred AI Certifications AWS Certified Generative AI Developer Professiona AWS Certified Machine Learning Engineer Associate Hands-on production experience takes priority over certifications.
This page is for Indian job seekers preparing for cloud engineer, DevOps, backend and solutions architect roles that list AWS as a skill, whether you are a fresher with a first certification or an experienced engineer moving into a senior role. It covers 60 questions in three levels: core services and concepts, then design and configuration choices for one to three years of experience, and finally scenario, security, cost and troubleshooting questions asked in senior rounds. Read each answer, explain it aloud in your own words, and try the CLI commands in a practice account with a billing alert set.
Questions you may be asked
What is the difference between an AWS Region and an Availability Zone?
Explain the AWS shared responsibility model.
What is Amazon EC2, and how do you read an instance type name like m7g.large?
What is Amazon S3, and what are its main storage classes?
