AI Engineer - Multiple Openings and Multiple Seniority Levels

Chenega Corporation•UNAVAILABLE, Georgia

2h•Remote

About The Position

Chenega Services & Federal Solutions, LLC, a Chenega Professional Services company, is seeking multiple AI Engineers to support the Centers for Disease Control and Prevention (CDC). These positions are contingent on contract award, and details may change based on the final award. The openings include two mid-level AI Engineer positions with a stated salary range of $82,000–$92,000 and one senior-level AI Engineer position with a stated salary range of $108,000–$118,000. Final salary depends on skill set, qualifications, and approved funding. The engineers will build, enhance, and support production generative AI applications and web-based AI solutions, using Python and Microsoft Azure services. Work includes building RAG systems and agent workflows, integrating AI applications with enterprise systems, evaluating and monitoring application quality and performance, and supporting production deployments. The senior-level role also includes developing advanced RAG systems, knowledge assistants, chatbots, and multi-turn conversational experiences, as well as implementing agent and multi-agent workflows. The company describes its workplace as offering management engagement, quality leadership, teamwork, recognition for performance, and promotion opportunities. Teleworking is permitted; the position is listed as remote within the United States. Employment is contingent on contract award.

Requirements

Mid-level: At least 3 years of technical experience, including developing and supporting production software applications.
Mid-level: At least 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: At least 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: At least 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, Foundry Agent Service, and related Azure services.
Implement RAG pipelines, including document processing, chunking, embeddings, search, context preparation, and source citations.
Build and improve advanced RAG systems, knowledge assistants, chatbots, and multi-turn conversational experiences.
Implement agent and multi-agent workflows using tool or function calling, structured outputs, conversation context, controlled execution, error handling, execution limits, 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 backend services using Python, web frameworks, containers, and Azure services.
Test, evaluate, and troubleshoot AI applications using datasets, logs, traces, and monitoring to assess 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 AI engineers, software engineers, architects, and DevSecOps teams to deliver maintainable production applications.

Benefits

Paid time off, including vacation accrual of 5.67 hours per pay period (136 hours annually).
11 paid holidays.
California residents receive an additional 24 hours of sick leave per year.
Medical insurance.
Dental insurance.
Vision insurance.
Prescription coverage.
Employee Assistance Program.
Short- and long-term disability insurance.
Life and AD&D insurance.
Flexible Spending Account.
Health Savings Account.
Health Reimbursement Account.
Dependent Care Spending Account.
Commuter benefits.
401(k) / 401(a) retirement plans.
Hospital indemnity coverage.
Critical illness coverage.
Accident insurance.
Pet insurance.
Legal insurance.
Identity theft protection.