Design and develop production-ready agentic AI systems using Python, LangChain, LangGraph, and Amazon Bedrock. Build super-agent and sub-agent architectures, tool-calling workflows, RAG solutions, REST API integrations, and scalable cloud services. Implement prompts, structured outputs, state management, validation, retries, fallbacks, and guardrails. Test and evaluate agent performance, and deploy solutions within AWS environments using software engineering best practices.
- 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.
- Strong 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.
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