Required Skills: Python, LLM Systems, Agentic AI, Data Pipelines, Distributed Systems
Role :
We’re looking for a strong AI Engineer to build and deploy applied AI systems from research and experimentation through production. This role combines LLM/agentic AI development, data pipelines, ML infrastructure, and production deployment in high-assurance, mission-critical environments. You’ll also work directly with U.S. Government and strategic partners, translating real-world mission requirements into reliable AI systems.
Responsibilities :
- Build and deploy agentic AI systems supporting Government missions.
- Design LLM applications including multi-agent systems, tool-using agents, RAG, and human-in-the-loop workflows.
- Work directly with government partners to translate mission requirements into technical solutions.
- Build and maintain data curation, evaluation, and ML pipelines.
- Develop infrastructure for model inference, experimentation, evaluation, and deployment.
- Build AI systems that combine mission data, models, and tools to enable operational autonomy.
- Own systems end-to-end across architecture, development, deployment, reliability, security, and continuous improvement.
Require Skills :
- Strong Python engineering skills with end-to-end ownership experience.
- Hands-on experience with LLMs and agentic AI systems.
- Experience building or maintaining data pipelines / ML infrastructure.
- Experience with distributed systems or production-scale infrastructure.
- Familiarity with RL workflows or simulation environments.
- Ability to work independently in ambiguous, partner-facing environments.
- Comfortable working in high-security and high-reliability environments.
Preferred Qualifications
- Active U.S. security clearance or eligibility and willingness to obtain one.
- Experience supporting U.S. Government, defense, intelligence, or national security missions.
- Experience with defense/government-focused technology companies or startups.
- Background working in fast-moving startup, mission-critical government, or similar environments.