Remote AI Engineer — Agentic LLMs for Government

Crossing Hurdles; United States; Remote; USD 300,000 - 500,000; Full time

Job summary

Crossing Hurdles is seeking a remote, full-time AI engineer to build and deploy agentic AI systems supporting U.S. Government missions.

The role involves end-to-end ownership from prototype to production, with a focus on Python engineering, LLMs, and robust data pipelines. You will design multi-agent LLM applications, collaborate with government partners to translate mission needs into technical solutions, and develop infrastructure for model inference, experimentation, and deployment in

Qualifications

• Proficient Python engineering with end-to-end ownership.
• Experience with large language models and agentic systems.
• Built or maintained ML data pipelines and infrastructure.
• Familiar with RL workflows or simulation environments.
• Comfortable working in high-security/high-reliability settings.

Responsibilities

• Build and deploy agentic AI systems for U.S. Government missions from prototype to production.
• Design and integrate LLM applications, including multi-agent systems and human-in-the-loop autonomy.
• Collaborate with government partners to translate mission needs into technical systems.
• Develop large-scale data curation and evaluation pipelines for model training.
• Create infrastructure for model inference, experimentation, evaluation, and deployment.
• Own systems across the full lifecycle, including discovery, architecture, implementation, and security.

Skills

Python engineering

LLMs

Agentic systems

Data pipelines

RL workflows

High-security environment

Job description

Crossing Hurdles is seeking a remote, full-time AI engineer to build and deploy agentic AI systems supporting U.S. Government missions.

The role involves end-to-end ownership from prototype to production, with a focus on Python engineering, LLMs, and robust data pipelines. You will design multi-agent LLM applications, collaborate with government partners to translate mission needs into technical solutions, and develop infrastructure for model inference, experimentation, and deployment in