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# Generative AI Engineer

- **Company:** JRSS, INC.
- **Location:** United, WV, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Automation of Tests, Microsoft Azure, Code Review, Databases, Continuous Integration, Python (Programming Language), Open Source Technology, OpenAI, Cloud Services, Systems Integration, TypeScript, Enterprise Search, Microsoft Agent Framework, Data Logging, QLoRA, Chatbots, ReactJS, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Langfuse — LLM Observability and Analytics Platform, Git, Fastapi, AngularJS, Information Technology, Low Latency, SGLang, PydanticAI Framework, Machine Learning Operations, Front End Software Development, Evaluation of Large Language Models, Invoking Functions, Model Context Protocol, VLLM, Devsecops, Docker, Natural Language Generation
- **Published:** November 9, 2026
- **Apply:** https://www.thejobnetwork.com/job/b3eb59a1-84a2-4bdf-beac-e0a9b2bc9041/generative-ai-engineer

## About the Role

* Bachelor's degree in Computer Science, Engineering, or a related field.
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 * 3+ years of technical experience, including developing and supporting production software applications.
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 * 1+ year of hands-on generative AI development, including RAG-based assistants or chatbots.
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 * Experience with Python backend development, including asynchronous APIs, using FastAPI, Pydantic, or comparable tools.
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 * Experience with Azure, including Azure OpenAI, Azure AI Search, and integration with databases and external services.
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 * Hands-on RAG experience with document processing, chunking, embeddings, vector or hybrid search, and citations.
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 * Demonstrated agent workflow experience with tool calling, structured outputs, and context management using PydanticAI, Microsoft Agent Framework, LangGraph, Foundry Agent Service, or similar tools.
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 * Ability to conduct LLM evaluation and troubleshooting using test datasets, logs, and traces to assess quality, latency, and cost.
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 * Experience with Git, code reviews, automated testing, CI/CD, and Agile team delivery.
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 * Must be a U.S. Citizen or Lawful Permanent Resident (Green Card holder).
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 * Ability to obtain and maintain a Public Trust or Suitability/Fitness determination.
\n, * Experience with Langfuse, Pydantic Logfire, or similar AI observability tools, and OpenTelemetry.
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 * Experience with advanced RAG techniques, multi-agent workflows, or MCP integrations.
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 * Experience with Docker, Azure deployment services, or frontend development using React, Angular, or TypeScript.
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 * Knowledge of open-source LLM hosting using vLLM, SGLang, or similar frameworks.
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 * Knowledge of LLM fine-tuning and optimization, including PEFT, LoRA/QLoRA, and quantization.
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 * Prior experience supporting CDC or other federal public health programs.
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## Description

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 * Build, enhance, and support production generative AI applications and web-based AI solutions.
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 * Develop Python backend services and AI integrations using Azure OpenAI, Azure AI Foundry, and related Azure services.
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 * Implement retrieval-augmented generation (RAG) pipelines, including document processing, chunking, embeddings, search, context preparation, and source citations.
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 * Implement agent workflows using tool/function calling, structured outputs, conversation context, error handling, execution limits, and human review where needed.
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 * Integrate AI applications with APIs, enterprise data sources, databases, cloud services, and Model Context Protocol (MCP)-based tools.
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 * Test and evaluate retrieval relevance, answer correctness and grounding, reliability, latency, and cost.
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 * Instrument applications with logging, tracing, and metrics to monitor model calls, retrieval steps, tool execution, errors, and token usage.
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 * Troubleshoot application issues, fix defects, and support deployments and production monitoring.
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 * Participate in code reviews, document implementation decisions, and collaborate with engineering and DevSecOps teams.
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