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CTEC is a leading technology firm that provides modernization, digital transformation, and application development services to the U.S. Federal Government. Headquartered in McLean, VA, CTEC has over 300 team members working on mission-critical systems and projects for agencies such as the Department of Homeland Security, Internal Revenue Service, and the Office of Personnel Management. The work we do effects millions of U.S. citizens daily as they interact with the systems we build. Our best-in-class commercial solutions, modified for our customers’ bespoke mission requirements, are enabling this future every day. The Company has experienced rapid growth over the past 3 years and recently received a strategic investment from Main Street Capital Corporation (NYSE: MAIN). In addition to our recent growth in Federal Civilian agencies, we are seeking to expand our capabilities in cloud development and footprint in national-security focused agencies within the Department of Defense and U.S. Intelligence Community.   We are seeking to hire a AI/Machine Learning Engineer to our team! Role Overview: As an AI/ML Engineer for CTEC, you will develop Agentic AI systems designed to automate and optimize health benefits determinations for the Office of Personnel Management (OPM). Unlike traditional "black-box" models, your work will focus on marrying the reasoning capabilities of Large Language Models (LLMs) with deterministic, rule-driven patterns to ensure accuracy, auditability, and compliance in complex decision-making workflows. Duties and Responsibilities: Agentic System Architecture: Design and deploy autonomous AI agents capable of multi-step reasoning, tool-use, and self-correction to navigate complex federal benefit policies. Deterministic Logic Integration: Develop "Guardrail" layers that synchronize probabilistic LLM outputs with rigid business rules, ensuring benefit determinations adhere strictly to legal and regulatory frameworks. RAG & Knowledge Engineering: Implement advanced Retrieval-Augmented Generation (RAG) solutions, utilizing layout-aware parsing to extract information from dense manuals/documentation and unstructured data. Hybrid Model Development: Design and evaluate machine learning models that support both data-driven predictions and symbolic/rule-based automation. MLOps & Agent Monitoring: Deploy models into cloud environments with a focus on LLM-specific observability (tracing reasoning loops, monitoring for hallucinations, and detecting data drift in logic). Auditability & Explainability: Ensure every AI-driven determination has a clear, human-readable "audit trail" or reasoning chain that justifies the outcome based on source documentation. Collaboration: Work alongside solution architects and business stakeholders to translate complex health insurance policies into executable AI logic. Skills & Work Experience: Professional Experience: 5+ years in Machine Learning or Data Science, with at least 2 years of hands-on experience with LLM orchestration and Generative AI frameworks. Agentic Frameworks: Proficiency with tools such as LangChain, LangGraph, CrewAI, or Semantic Kernel for building multi-step agent workflows. Core Development: Strong proficiency in Python and experience with standard frameworks (PyTorch, TensorFlow, or Scikit-learn) Data Engineering: Strong SQL skills and experience with distributed data processing (Spark/PySpark) to handle large-scale enterprise data. Analytical Rigor: Ability to debug non-deterministic systems and implement rigorous evaluation frameworks (e.g., RAGAS, LLM-as-a-judge) data platforms. Preferred: Experience with Azure Machine Learning, Azure AI services, or similar cloud AI platforms. Experience implementing Generative AI, LLM, or RAG-based solutions. Experience supporting federal IT modernization or data transformation programs. Familiarity with healthcare, insurance, or benefits adm