Hybrid | Forward Deployed Engineer, U.S. Government — $300,000–$500,000/year

24-Mag•Washington D.C., WA

19h•Hybrid

About The Position

24-MAG LLC is offering a full-time opportunity for an experienced Forward Deployed Engineer to build and deploy mission-critical AI systems supporting U.S. Government environments. The role spans applied AI research, production engineering, infrastructure, and forward-deployed implementation. The engineer will work directly with government partners to translate operational requirements into secure, reliable AI systems and own work from discovery and architecture through deployment, reliability, security, and iteration. The position involves work across Python engineering, LLM applications, agentic AI, data pipelines, distributed systems, infrastructure, and production deployment. It requires technical and operational ownership in ambiguous, partner-facing, high-security, high-assurance environments. Priorities may evolve with mission requirements and partner needs, and the work may span research-oriented experimentation as well as production systems. The engagement is hybrid in the Washington, D.C. area, and relocation to that area is required. The stated base compensation is $300,000–$500,000 per year. The role involves forward-deployed work with U.S. Government partners, with security, reliability, and operational ownership central to the position.

Requirements

Strong Python engineering skills with demonstrated end-to-end ownership.
Experience building production applications using LLMs.
Hands-on experience with agentic or multi-agent AI systems.
Experience building or maintaining data pipelines or ML infrastructure.
Strong understanding of distributed systems.
Comfortable operating in high-security, high-reliability, or mission-critical environments.
Strong ability to work independently in ambiguous, partner-facing situations.
Relocation to the Washington, D.C. area is required.

Nice To Haves

Familiarity with reinforcement-learning workflows or simulation environments.
An active U.S. security clearance is highly advantageous; candidates eligible and willing to obtain a U.S. security clearance may also be considered.
Experience supporting U.S. Government, defense, intelligence, or national-security missions is preferred.
Experience in startups, defense organizations, government-focused technology companies, or comparable fast-moving environments is beneficial.

Responsibilities

Build and deploy advanced AI systems supporting high-impact U.S. Government missions.
Work directly with government partners in forward-deployed environments.
Translate mission requirements into practical technical systems.
Take projects from prototype and experimentation through production deployment.
Maintain technical and operational ownership in ambiguous partner-facing environments.
Design and integrate production-grade LLM applications.
Build multi-agent and tool-using AI systems.
Develop retrieval-augmented generation workflows.
Implement human-in-the-loop autonomy where appropriate.
Convert mission data, models, and tools into operational AI capabilities.
Build and maintain large-scale data-curation pipelines.
Develop evaluation infrastructure for model training and improvement.
Create systems supporting inference, experimentation, evaluation, and deployment.
Maintain scalable ML and data workflows across advanced AI platforms.
Support reliable operation as mission and model requirements evolve.
Design and operate distributed systems in high-reliability environments.
Own architecture, implementation, deployment, and production iteration.
Build systems with security, resilience, and operational continuity in mind.
Diagnose system failures and improve reliability and performance.
Apply sound engineering judgment to infrastructure and deployment trade-offs.
Operate independently in high-security and high-assurance environments.
Collaborate with technical, operational, and government stakeholders.
Maintain ownership throughout the full system lifecycle.
Adapt rapidly as mission requirements and partner needs evolve.
Balance development speed with security, reliability, and operational effectiveness.