> Markdown version of [/jobs/ext/3545078-gen-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3545078-gen-ai-engineer). 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). --- # Gen AI Engineer - **Company:** PHOTON, LLC - **Location:** Irving, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Artificial Intelligence, Continuous Integration, Data Retrieval, Monitoring of Systems, Python (Programming Language), PostgreSQL, Machine Learning, Open Source Technology, OpenAI, Tensorflow, Search Technologies, Software Engineering, Workflow Management Systems, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Security, Llamaindex, Generative AI, Backend, Fastapi, Containerization, AI Platforms, HuggingFace, Machine Learning Operations, Claude, Asynchronous Programming, Google Gemini, Automation Anywhere, Api Management, Docker - **Published:** September 30, 2026 - **Apply:** https://www.dice.com/job-detail/2300eb41-3784-4d71-9755-69ebe71e43e8 ## About the Role * Hands-on experience building and deploying GenAI applications in a production setting. * Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.). * Experience with vector search, embedding models, and advanced data retrieval patterns. * Knowledge of model fine-tuning techniques and local LLM quantization/hosting. * Familiarity with production-grade monitoring, API security, and CI/CD for ML. ## Description Role Summary: We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices. Technical Stack: * LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment. * Frameworks: LangChain, LlamaIndex, and Hugging Face. * Orchestration: LangGraph and Multi-Agent Systems (MAS). * Development: Python, FastAPI, and Asynchronous Programming. * RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies. * ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. * Deployment: Docker, Production API management, and LLM monitoring. * Tools: Prompt Engineering, Workflow Design, and GenAI Optimization., * Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures. * Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval. * Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements. * Design and deploy high-performance, scalable backend services using FastAPI and Async Python. * Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance. * Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools. * Containerize and deploy AI services via Docker to production environments.