> Markdown version of [/jobs/ext/3688138-ai-agentic-developer](https://www.wearedevelopers.com/jobs/ext/3688138-ai-agentic-developer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# AI Agentic Developer

- **Company:** Diverse Lynx LLC
- **Location:** Lake Angelus, MI, United States
- **Salary:** $83,200.0 - $93,600.0
- **Contract:** Temporary to permanent
- **Skills:** LangGraph Framework, Java (Programming Language), Amazon Web Services, Microsoft Azure, Computer Programming, Continuous Integration, Python (Programming Language), Software Engineering, TypeScript, Pinecone, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Agentic-AI, Containerization, Kubernetes, AutoGen, Machine Learning Operations, FAISS, Claude, Google Gemini, Semantic Kernel, GPT, Docker, ChromaDB, Web Api, Microservices
- **Published:** October 11, 2026
- **Apply:** https://www.careerjet.com/job/usf45ce096a5ac4e4fc57886688b4b50ef/eaa

## About the Role

* Core Engineering & Programming: Strong software engineering fundamentals with expert-level proficiency in Python. Experience with Java, Go, or TypeScript is a strong plus.
 * LLM & GenAI Application Development: Proven, hands-on experience building and deploying production-grade applications using Large Language Models (LLMs) like GPT, Claude, or Gemini. This must go beyond simple API calls and include experience with tool/function-calling, structured outputs, and evaluation.
 * Agentic AI Frameworks & Orchestration: Demonstrable expertise in designing and implementing agentic workflows using frameworks like LangGraph, Semantic Kernel, AutoGen, or similar.
 * Experience with multi-agent systems, planning, and autonomous execution is critical.
 * RAG (Retrieval-Augmented Generation): Deep, practical knowledge of building and Eligible to workimizing RAG pipelines. This includes data ingestion, various chunking strategies, embeddings, vector databases (e.g., Pinecone, Chroma, FAISS), and hybrid search/reranking.
 * Production & MLOps: Experience with production engineering practices, including building scalable APIs (REST, RPC), microservices, CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, Azure, or Eligible to workP).