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Gallatin is rebuilding logistics infrastructure for U.S. national security missions. As Applied AI Engineer for Internal Systems, you'll own the engineering behind AI tools that Gallatin uses to run the company, working alongside the Internal AI Operations Product Manager.
You'll build AI systems and agents that remove operational overhead and enable faster decision-making. Projects could include a coding agent harness, an internal document assistant with access controls, an agent-native CRM tailored to defense business, or agents that keep project status current across existing systems.
Responsibilities include:
- Converting workflow problems into technical requirements and identifying process improvements before automating
- Building applications and integrations connecting LLM models to company systems, using conventional code where predictability is critical
- Turning experiments into production software with evaluations, observability, and failure recovery
- Designing access controls and human review for workflows handling sensitive or consequential information
- Instrumenting systems to measure time saved and output quality, using failure data to improve software
- Documenting systems so other engineers can maintain them
- Collaborating with nontechnical colleagues to understand bottlenecks and shape solutions
Your first 90 days will involve selecting an initial workflow with an end-user team, establishing a baseline, owning technical design, shipping a working version with evaluations and observability, and iterating based on regular use.
Gallatin values clear thinking, direct communication, and ownership that doesn't stop until something works. The mission is creating decision advantage in the highest-stakes environments—defense operations and disaster response.
REQUIREMENTS:
- Shipped software that people use and maintained it post-launch
- 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; ability to explain testing and failure modes
- Full-stack experience sufficient to build a usable interface and deploy the service
- Engineering judgment about reliability and security; knowing when a model needs human review vs. simpler implementation
- Comfort working directly with nontechnical colleagues and explaining tradeoffs clearly
- Helpful: experience building internal tools, evaluating AI outputs, or integrating business systems
- U.S. citizenship required (proof required prior to employment)
- Ability to obtain and maintain U.S. government security clearance; ability to work in classified environments when necessary
