- Responsibilities
- Design and develop AI agents, super-agent/sub-agent architectures, and agentic workflows using Python, LangChain, and LangGraph.
- Build production-ready AI solutions using Amazon Bedrock.
- Design and implement super-agent and sub-agent patterns for complex, multi-step business workflows.
- Develop agents capable of tool calling, API invocation, information retrieval, and multi-step task execution.
- Design prompts, tool definitions, structured outputs, agent state, and workflow orchestration.
- Integrate AI agents with REST APIs, databases, enterprise applications, and external services.
- Build RAG-based solutions using embeddings, vector databases/search, and enterprise knowledge sources.
- Implement reliability mechanisms including error handling, retries, validation, fallback strategies, and guardrails.
- Test, evaluate, and improve agent responses for accuracy, reliability, and consistency.
- Develop clean, scalable, and maintainable Python services and APIs.
- Work with senior engineers and architects to integrate and deploy AI solutions into AWS cloud environments.
- Required Skills
- Approximately 5 years of software development experience.
- Strong Python programming skills.
- Hands-on experience building Generative AI / LLM applications.




- Practical experience building and implementing AI agents or agentic workflows.
- Mandatory hands-on experience with Amazon Bedrock.
- Strong understanding of super-agent and sub-agent concepts, architectures, and orchestration.
- Experience with LangChain and/or LangGraph.
- Experience integrating LLMs through APIs.
- Hands-on experience with RAG, embeddings, vector databases, and vector search.
- Strong understanding of:
- Prompt engineering
- Function/tool calling
- Structured LLM outputs
- Agent state and workflow orchestration
- Super-agent/sub-agent patterns
- RAG fundamentals
- Embeddings and vector search
- LLM response validation and error handling

- Experience building and consuming REST APIs.
- Solid understanding of software engineering fundamentals, Git, testing, debugging, and code quality.
- Preferred Skills
- Experience with Amazon Bedrock AgentCore.
- Experience with AWS services such as:

- AWS Lambda
- Amazon S3
- API Gateway
- DynamoDB
- IAM
- CloudWatch

- Experience deploying AI applications using Docker and AWS cloud services.
- Exposure to MCP (Model Context Protocol) or similar AI tool-integration protocols.
- Experience with LLM evaluation, observability, tracing, or agent performance monitoring.

📌 AI Engineer – Agentic AI | Offshore (Tamil Nadu)
🏢 Photon
📍 Tamil Nadu