AI Engineer - Multiple Openings and Multiple Seniority Levels

Chenega Services & Federal Solutions•Town of Gates, NY

1h•$82,000 - $118,000•Remote

About The Position

Chenega Services & Federal Solutions, LLC is hiring multiple AI Engineers to support the Centers for Disease Control and Prevention (CDC). This position is contingent on contract award, and the details may change based on the final award. Openings include two mid-level positions with a stated salary range of $82,000–$92,000 and one senior-level position with a stated salary range of $108,000–$118,000. The position is remote within the United States. The company describes an employee benefits program, engaged management, quality leadership, teamwork, performance recognition, and promotion opportunities. Mid-level AI Engineers build, enhance, and support production generative AI applications and web-based AI solutions. They develop Python backend services and AI integrations using Azure OpenAI, Azure AI Foundry, and related Azure services; implement RAG pipelines and agent workflows; integrate with APIs, enterprise data, databases, cloud services, and MCP-based tools; and evaluate, monitor, troubleshoot, and support deployments of AI applications. Senior-level AI Engineers build, enhance, and support production generative AI applications and web-based AI solutions, including advanced RAG systems, knowledge assistants, chatbots, and multi-turn conversational experiences. They develop secure cloud-native backend services and agent or multi-agent workflows, and collaborate with AI engineers, software engineers, architects, and DevSecOps teams to deliver maintainable production applications. Final salary determination is based on skill set, qualifications, and approved funding. Some benefits depend on role, work schedule, or location.

Requirements

Mid-level: 3+ years of technical experience, including developing and supporting production software applications.
Mid-level: 1+ year of hands-on generative AI development, including RAG-based assistants or chatbots.
Mid-level: Experience with Python backend development, including asynchronous APIs, using FastAPI, Pydantic, or comparable tools.
Mid-level: Experience with Git, code reviews, automated testing, CI/CD, and Agile team delivery.
Mid-level: Experience with Azure, including Azure OpenAI, Azure AI Search, and integration with databases and external services.
Mid-level: Ability to demonstrate agent workflow experience with tool calling, structured outputs, and context management using PydanticAI, Microsoft Agent Framework, LangGraph, Foundry Agent Service, or similar tools.
Mid-level: Ability to conduct LLM evaluation and troubleshooting using test datasets, logs, and traces to assess quality, latency, and cost.
Mid-level: Hands-on RAG experience with document processing, chunking, embeddings, vector or hybrid search, and citations.
Senior-level: 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.
Senior-level: 2+ years of hands-on, client-facing experience building generative AI solutions, including practical delivery of RAG-based knowledge assistants.
Senior-level: Strong Python application development experience, including asynchronous programming, APIs, backend services, and frameworks such as FastAPI and Pydantic or comparable tools.
Senior-level: 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.
Senior-level: Hands-on experience implementing agentic and multi-agent workflows, including intent-based routing, tool/function calling, structured outputs, context management, execution limits, and error handling.
Senior-level: 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.
Senior-level: Hands-on experience with Microsoft Azure, including Azure OpenAI and Azure AI Search, and deploying or integrating applications with cloud-hosted services.
Senior-level: Experience with REST APIs, JSON-based interfaces, databases, and external service integrations.
Senior-level: Experience evaluating and troubleshooting LLM applications using test datasets, tracing, and monitoring to assess answer correctness, retrieval quality, latency, and cost.
Senior-level: Experience using modern software-development practices, including Git, pull requests, code reviews, automated testing, CI/CD, issue tracking, and Agile/Scrum delivery practices.
Senior-level: 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.

Responsibilities

Build, enhance, and support production generative AI applications and web-based AI solutions.
Develop Python backend services and AI integrations using Azure OpenAI, Azure AI Foundry, and related Azure services.
Implement RAG pipelines, including document processing, chunking, embeddings, search, context preparation, and source citations.
Implement agent workflows using tool/function calling, structured outputs, conversation context, error handling, execution limits, and human review where needed.
Integrate AI applications with APIs, enterprise data sources, databases, cloud services, and MCP-based tools.
Test and evaluate retrieval relevance, answer correctness and grounding, reliability, latency, and cost.
Add logging, tracing, and metrics to monitor model calls, retrieval steps, tool execution, errors, and token usage.
Troubleshoot application issues, fix defects, and support deployments and production monitoring.
Participate in code reviews, document implementation decisions, and collaborate with engineering and DevSecOps teams.
Develop AI applications using Azure AI Foundry, Azure OpenAI, and Foundry Agent Service.
Build and improve advanced RAG systems, knowledge assistants, chatbots, and multi-turn conversational experiences.
Implement agent and multi-agent workflows using tool/function calling, structured outputs, controlled execution, guardrails, and human review where needed.
Develop secure, cloud-native backend services using Python, web frameworks, containers, and Azure services.
Work with AI engineers, software engineers, architects, and DevSecOps teams to deliver maintainable production applications.