Production LLM Engineer: Secure Systems & Secret Clearance

Stott and May; Washington (District of Columbia); On-site; USD 150,000 - 210,000; Full time

Job summary

Stott and May in Washington, DC, seeks a Machine Learning Engineer to design, deploy, and operate production LLM systems in secure, air-gapped environments. This role involves building end-to-end AI capabilities for mission-critical applications in national security contexts. You will own end-to-end systems, work across data pipelines to inference, and ship features into secure environments.

A U.S. Secret clearance is required; sponsorship is not provided; the team emphasizes high ownership and

Qualifications

• Experience deploying ML systems into production.
• Hands-on with LLMs, vector databases, or agentic workflows.
• Comfortable owning systems end-to-end in a fast-moving environment.
• Active U.S. Secret clearance (TS/SCI preferred).
• No Sponsorship provided.

Responsibilities

• Designing and deploying LLM systems (RAG, embeddings, vector search).
• Building APIs and backend services for AI-powered applications.
• Working across the full stack from data pipelines to inference.
• Shipping systems into secure, sometimes air-gapped environments.

Skills

Production ML systems

LLMs

Vector databases

End-to-end ownership

Clearance (US Secret)

Sponsorship not provided

Tools

Vector databases

Job description

Stott and May in Washington, DC, seeks a Machine Learning Engineer to design, deploy, and operate production LLM systems in secure, air-gapped environments. This role involves building end-to-end AI capabilities for mission-critical applications in national security contexts. You will own end-to-end systems, work across data pipelines to inference, and ship features into secure environments.

A U.S. Secret clearance is required; sponsorship is not provided; the team emphasizes high ownership and