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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Chenega Corporation - **Location:** Atlanta, GA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Application Integration Architecture, Application Services, Automation of Tests, Microsoft Azure, Cloud Engineering, Code Review, Databases, Continuous Integration, Information Engineering, Web Development, Issue Tracking Systems, JSON, Python (Programming Language), Machine Learning, OAuth, Open Source Technology, OpenID, OpenAI, Azure Active Directory, Cloud Services, JSON Web Token, Search Technologies, Software Engineering, TypeScript, Web Applications, Web Application Frameworks, Enterprise Search, Microsoft Agent Framework, Data Processing, Chatbots, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Backend, Git, Fastapi, AngularJS, Information Technology, Low Latency, PydanticAI Framework, Machine Learning Operations, Front End Software Development, Asynchronous Programming, Invoking Functions, Restful APIs, Model Context Protocol, Devsecops, Docker, Container Apps, Natural Language Generation - **Published:** November 3, 2026 - **Apply:** https://www.thejobnetwork.com/job/2e683f21-c1c8-494c-b50b-232cdb9c4ff4/ai-engineer ## About the Role * Must be currently located in Atlanta, GA or Washington, DC and able to work onsite when needed. * 5+ years of hands-on technical experience in software engineering, web application development, data engineering, data science, machine learning, or a related technical discipline, including building, deploying, and supporting production applications * 2+ years of hands-on, client-facing experience building generative AI solutions, including practical delivery of RAG-based knowledge assistants * Strong Python application development experience, including asynchronous programming, APIs, backend services, and frameworks such as FastAPI and Pydantic or comparable tools * Hands-on experience with advanced RAG techniques, including document chunking, embeddings, hybrid keyword/vector search, semantic ranking, query rewriting, retrieval filters, multi-turn context, and source citations * Hands-on experience implementing agentic and multi-agent workflows, including intent-based routing, tool/function calling, structured outputs, context management, and execution limits and error handling * Experience with at least one agent framework, such as PydanticAI, Microsoft Agent Framework, LangGraph, or a comparable framework, and with Azure AI Foundry Agent Service * Hands-on experience with Microsoft Azure, including Azure OpenAI and Azure AI Search, and deploying or integrating applications with cloud-hosted services * Experience with REST APIs, JSON-based interfaces, databases, and external service integrations * Experience evaluating and troubleshooting LLM applications using test datasets, tracing, and monitoring to assess answer correctness, retrieval quality, latency, and cost * Experience using modern software-development practices, including Git, pull requests, code reviews, automated testing, CI/CD, issue tracking, and Agile/Scrum delivery practices * Ability to obtain and maintain a Public Trust or suitability determination, as required by the client or contract * Bachelor's degree in computer science, engineering, or a related field Preferred: * Experience with containerized or cloud-native application development, including Docker, Azure Container Apps, Azure App Service, or similar services * Experience with Model Context Protocol (MCP) integrations for connecting agents to enterprise tools and data sources * Experience with enterprise authentication and authorization technologies, such as OAuth2/OIDC, JWT, Microsoft Entra ID, and MSAL * Experience with frontend application development using TypeScript, Angular, React, or similar technologies * Experience working in government, healthcare, or other regulated environments * Exposure to fine-tuning, hosting, or serving open-source LLMs and working with model-serving frameworks * Knowledge of AI governance, responsible AI principles, prompt-injection defenses, and controls for protecting enterprise data and tool access * Ability to work effectively in a cross-functional engineering environment and clearly communicate technical problems, implementation decisions, and trade-offs to technical and non-technical stakeholders Final salary determination based on skill-set, qualifications, and approved funding. ## Description In this role, you will define and deliver mission-critical AI-enabled web applications that address complex, high-impact client challenges. You will apply modern generative AI techniques to design and build retrieval-based knowledge assistants, chatbot experiences, and agentic workflows that integrate data processing, orchestration, and multi-agent components behind intuitive user interfaces. You will collaborate with enterprise and solutions architects, product owners, AI engineers, data engineers, and DevSecOps teams to deliver secure, scalable, and maintainable production solutions. Your technical expertise will help clients translate emerging AI capabilities into practical applications that improve how people access information, make decisions, and carry out their work., * Build and enhance production generative AI applications and web-based AI solutions. * Develop AI applications using Azure AI Foundry, Azure OpenAI, and Foundry Agent Service. * Build retrieval-augmented generation (RAG) systems, knowledge assistants, chatbots, and agentic workflows. * Implement agent and LLM workflows that use tools, structured outputs, guardrails, and human review where needed. * Integrate AI applications with APIs, enterprise data sources, databases, cloud services, and MCP-based tools. * Develop secure, cloud-native application services and user interfaces using Python, web frameworks, containers, and Azure services. * Test, evaluate, and monitor AI applications to improve quality, reliability, latency, and cost. * Work with AI engineers, software engineers, architects, and DevSecOps teams to deliver maintainable production applications.