AI / Machine Learning Engineer

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 administration data environments.
• Experience applying data governance, privacy, and security best practices in AI/ML solutions.

Education:

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related discipline. Master’s degree preferred. Equivalent professional experience will be considered in lieu of a degree.

Clearance:
Must be a U.S. citizen and be able to obtain an OPM Public Trust clearance.

• Highlight any FedRAMP‑compliant cloud experience (e.g., deploying TensorFlow or PyTorch models on AWS GovCloud, using IAM roles, encrypted S3 buckets, and CI/CD pipelines with CodePipeline). Include a brief, quantifiable example in your résumé, such as “Reduced model inference latency by 30% on a secure AWS GovCloud pipeline for DHS data ingest.”

• Build a single‑page portfolio that showcases at least two end‑to‑end projects relevant to federal missions: one that processes sensitive or PII data (demonstrating data anonymization, audit logging, and role‑based access) and another that integrates a real‑time ML inference service into an existing legacy system (e.g., a SOAP‑based API). Link to the code (private repo with read‑only token) and provide a short video walkthrough of the deployment workflow.
• Prepare to discuss model governance and compliance during interviews: be ready to explain how you would version‑control datasets, enforce model traceability, and meet NIST AI risk management standards. Cite specific tools you’ve used—MLflow for tracking, DVC for data versioning, and Snyk or Checkmarx for security scanning of code.
• Emphasize any government or contracting background (security clearance, previous contracts with DHS, IRS, OPM, etc.). If you hold a clearance, list it prominently; if not, note “eligible for Secret/Top‑Secret clearance” and describe experience navigating acquisition or procurement processes, which signals you can hit the ground running on CTEC’s mission‑critical projects.
• When negotiating, reference the remote U.S. market for senior AI/ML engineers on federal contracts (typically $130‑$165 k base plus 10‑15% locality pay). Cite recent salary surveys (e.g., Levels.fyi, Glassdoor) and be prepared to request a structured “mission‑critical bonus” or additional paid time off that aligns with CTEC’s fast‑growth environment and the added compliance workload.