About the Role

Every hour spent chasing information or moving it between tools is an hour someone can't spend on the work they were hired to do. This role exists to remove that overhead.

As the Applied AI Engineer, Internal Systems you'll own the engineering behind the AI tools Gallatin uses to run the company. You'll work alongside the Internal AI Operations Product Manager to shape solutions, with the technical design and implementation in your hands. You'll also own deployment and reliability. Your users will be your colleagues. Work together with them to understand the bottlenecks, connect information across internal tools, and build agents that carry tasks through to completion.

What You'll Build

Projects could include an entire coding agent harness, an assistant that answers questions from internal documents with sources and access controls, an agent-native CRM custom to Gallatin's defense business, or an agent that keeps project status current across the systems teams already use. You'll help the product manager scope these projects by assessing technical feasibility and the cost of building and maintaining them.

You'll own the engineering from first prototype through production:

• Turn workflow problems into technical requirements. Identify where a process should change before automating it.
• Build applications and integrations that connect models to company systems. Use conventional code where the behavior needs to be predictable.
• Turn useful experiments into software people can depend on, with evaluations, observability, and a way to recover when something fails.
• Design access controls and human review into workflows that handle sensitive information or take consequential actions.
• Instrument the systems so we can measure time saved and output quality. Use failure data to improve the software and give the product manager evidence for what to improve or retire.
• Help colleagues use and extend what you build. Document the systems so another engineer can maintain them.

What We're Looking For

You've shipped software people use and maintained it after launch. You can turn an unclear problem into a technical plan and ship a useful first version.

• Strong programming ability in Python or TypeScript, with experience building APIs and working with databases.
• Experience building LLM applications or agents that use external tools and data. You can explain how you tested them and where they fail.
• Enough experience across the stack to build a usable interface and deploy the service behind it.
• Engineering judgment about reliability and security. You know when a model needs a human check and when a simpler implementation will work better.
• Comfort working directly with nontechnical colleagues. You can explain a tradeoff clearly and change your approach when the workflow demands it.

Experience building internal tools, evaluating AI outputs, or integrating business systems would help.

Your First 90 Days

Work with the Internal AI Operations Product Manager to choose an initial workflow with a team that will use it. Establish a baseline together. Own the technical design and ship a working version with evaluations and observability, then improve it through regular use. By day 90, we should know whether it saves time and meets the team's quality bar, with a plan to keep it working.

Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment.

This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary.