greenhouse
AI / Machine Learning Engineer
CyberMedia Technologies
Remote, USA
Full-time
Senior · 5+ years experience
Remote
Discovered 1 weeks ago
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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.
Key Skills for This Role
PythonPyTorchTensorFlowScikit-learnLangChainLangGraph
Full Job Posting
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.
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