Gallatin AI
AI Engineer – Decision & Optimization Systems
About this role
Gallatin seeks an AI Engineer to design and deploy feasibility and constraint systems that validate AI-generated logistics plans against policy, authority, and operational rules before execution. You'll bridge AI reasoning and real-world decision-making for national security logistics operations, ensuring plans are auditable, enforceable, and compliant.
What you'll do
- Design and implement feasibility models that encode command hierarchy, authority limits, and policy rules into formal constraints
- Build a centralized constraint framework for optimization and planning systems covering authority, compatibility, timing, and asset availability
- Own data validation pipelines and contracts to ensure robust constraint enforcement with incomplete or noisy operational data
- Integrate feasibility checks into real-time and batch planning pipelines alongside optimization and execution teams
- Create diagnostics and explainability layers so humans and systems understand why plans pass or fail validation
- Validate system behavior through scenario testing, simulations, and operational feedback from end users
What they're looking for
- Python, Java, Go, or C++
- Constraint-based or rule-based systems in production
- Linear and mixed-integer programming
- Optimization solvers (Gurobi, CPLEX, OR-Tools, Pyomo, AMPL)
- Integrating AI/agent outputs into deterministic decision pipelines
- Systems thinking and failure analysis
- Data validation and pipeline design
- Operations research or applied mathematics background
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Gallatin AI
Gallatin AI builds AI-powered logistics optimization systems for national security and defense operations, focusing on feasibility validation, allocation algorithms, routing networks, and supply chain decision-making. The company is hiring AI Engineers, Backend Engineers, and Infrastructure Engineers to design scalable, secure systems that ensure military logistics plans are compliant, auditable, and operationally robust.
View all jobs at Gallatin AILikely interview questions
- Walk us through a time you built a constraint-based or rule-based system in production. How did you handle cases where constraints conflicted, and how did you surface that to users?
- Describe your experience with optimization solvers (Gurobi, CPLEX, OR-Tools, etc.). How have you integrated their outputs into a downstream system that needed to enforce feasibility or policy rules?