Gallatin AI
AI Software Engineer
About this role
Gallatin seeks an AI/ML Software Engineer to develop and deploy intelligent logistics systems for national security applications. You'll own the full AI lifecycle—from model building and MLOps pipeline creation to real-time inference and performance monitoring—while collaborating with warfighters and cross-functional teams to deliver mission-critical AI solutions.
What you'll do
- Develop and deploy AI/ML models aligned with core product needs, including model validation through real-world testing
- Build scalable MLOps pipelines and real-time inference services for training, deployment, and monitoring
- Integrate structured and unstructured data sources from APIs, logs, and third-party systems to improve model quality
- Ensure high model reliability and uptime through comprehensive monitoring and observability
- Stay current with AI trends and emerging technologies to continuously improve model performance
- Mentor junior engineers and share AI/ML best practices across the organization
What they're looking for
- Python, C++, or C programming
- PyTorch, TensorFlow, CUDA, and Jupyter Notebooks
- Large Language Models (LLMs) and open-source model frameworks
- LangChain, RAG, Hugging Face, and prompt engineering
- Apache Kafka, Airflow, AWS Kinesis, pandas, and dbt
- Model monitoring, data drift detection, and observability tools (Grafana, Kibana)
- Deep neural networks, LLM security, and AI fundamentals
- MLOps and data pipeline architecture
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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 complete ML project you've built from model development to production deployment. What frameworks did you use, and how did you handle monitoring and reliability?
- Describe your experience with LLMs and RAG systems. Have you fine-tuned or deployed open-source models like Llama or Mistral, and what challenges did you encounter?