> Markdown version of [/jobs/ext/4562682-ai-developer-with-python](https://www.wearedevelopers.com/jobs/ext/4562682-ai-developer-with-python). 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 Developer with Python - **Company:** Robotics Technologies LLC - **Location:** Wilmington, DE, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** LangGraph Framework, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Django Web Framework, Python (Programming Language), Natural Language Processing, Web Application Frameworks, Google Cloud, Flask (Web Framework), Large Language Models, Prompt Engineering, Generative AI, Backend, Agentic-AI, Git, Fastapi, Build Management, Containerization, Production Code, CrewAI, Machine Learning Operations, Virtual Agents, Restful APIs, GPT, Data Pipelines, Docker, Web Api, Microservices - **Published:** November 4, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9eeb96ea43454d77 ## About the Role * 7+ years of total software experience with deep proficiency in Python. * Hands-on experience with GenAI, NLP, prompt engineering, and vector stores. * Knowledge of web frameworks such as FastAPI, Flask, or Django. * Familiarity with MLOps practices, Git version control, and containerization ## Description A Python AI Developer job description requires strong programming skills, Generative AI expertise, and backend API development. Key requirements typically include Python proficiency, LLM frameworks, and cloud deployment., * Design and build AI-driven applications, data pipelines, and microservices using Python. * Implement Retrieval-Augmented Generation (RAG) and orchestrate AI agents using tools like LangChain, LangGraph, or CrewAI. * Integrate Large Language Models (LLMs) with enterprise vector databases and external REST APIs. * Optimize backend performance and deploy production-ready code via Docker and cloud platforms (AWS, Azure, GCP).