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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI Engineer - **Company:** Industrial Staffing Services Incorporated - **Location:** Washington, DC, United States (Remote available) - **Experience:** Experienced - **Salary:** $54,080.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Linux, Amazon DynamoDB, Elasticsearch, Python (Programming Language), Machine Learning, MongoDB, Cloud Services, Azure Machine Learning, Software Engineering, Apache Solr, SQL Databases, Unstructured Data, Enterprise Data Management, Cloud Platform System, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Keras, Git, Information Technology, Machine Learning Operations, Virtual Agents, GPT, Automation Anywhere - **Published:** September 13, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18296142?backUrl=%2Fcareer%2F18296142%2FGenerative-Ai-Engineer-D-C-Washington ## About the Role * 2+ years of hands-on AI/ML engineering experience, including demonstrable experience developing LLM or Generative AI applications. * Strong Python programming skills. * Working knowledge of SQL. * Hands-on experience building and deploying LLM or Generative AI solutions in real-world applications. * Strong understanding of RAG, embeddings, prompt engineering, structured outputs, and LLM APIs. * Experience developing AI applications beyond experimentation or basic use of generative AI tools. * Experience with agentic AI, tool/function calling, or multi-step AI workflows preferred. * Experience with agent frameworks such as LangGraph, CrewAI, AutoGen, Strands, or comparable technologies preferred. * Experience with AWS or Azure AI services, including Amazon Bedrock, SageMaker, Azure OpenAI, or Azure AI Foundry preferred. * Experience with MLOps, vector databases, Elasticsearch/Solr, PyTorch, Keras, Git, Azure DevOps, Linux, MongoDB, or DynamoDB is a plus. * Strong analytical, problem-solving, and communication skills. * Ability to work independently and take ownership of AI/ML development activities. * U.S. Citizenship required. * Must be able to obtain and maintain a Federal Public Trust. * Federal Public Trust is not required prior to starting. * Ability to work remotely with occasional or quarterly travel to Washington, DC. Education * Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical discipline preferred. * Equivalent relevant professional experience may be considered in lieu of a degree. ## Description The Generative AI Engineer will design, develop, and deploy production-grade AI solutions using Large Language Models, retrieval-augmented generation (RAG), agentic AI, and machine learning. This role focuses on building practical AI applications that integrate LLMs, enterprise data, cloud services, APIs, and automation to address business and mission needs. The ideal candidate has hands-on experience taking AI/ML solutions from experimentation through deployment and ongoing improvement. This position requires strong Python skills, practical LLM application development experience, and the ability to obtain a Federal Public Trust., * Design and develop Generative AI and LLM-based applications, including RAG, summarization, structured extraction, embeddings, and natural-language interfaces. * Build agentic AI workflows that support tool and function calling, planning, task decomposition, and multi-step execution. * Develop and optimize Python-based AI/ML applications and data workflows. * Integrate LLM platforms and models such as Claude, GPT, Gemini, Llama, Amazon Bedrock, Azure OpenAI, or similar technologies. * Design and implement effective prompt-engineering strategies. * Build and maintain RAG pipelines, embedding workflows, vector retrieval, and search capabilities. * Evaluate AI solutions for accuracy, grounding, hallucinations, citation quality, reliability, and production performance. * Develop test sets, evaluation methods, and monitoring approaches for AI-enabled applications. * Deploy, troubleshoot, and support AI solutions in distributed and cloud environments. * Integrate APIs, SQL, structured data, unstructured data, and enterprise data sources into AI applications. * Collaborate with technical and business teams to translate requirements into practical AI solutions. * Participate in the full AI/ML lifecycle, including requirements gathering, prototyping, development, deployment, monitoring, and continuous improvement. * Take ownership of assigned AI/ML solutions and clearly communicate technical approaches, implementation decisions, and individual contributions. ## Related Videos - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Livecoding with AI](https://www.wearedevelopers.com/videos/1201-livecoding-with-ai) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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