MLOps Engineer – Production ML in Secure Deployments

Gallatin AI, Inc.; El Segundo (CA); On-site; USD 80,000 - 210,000; Full time

Benefits offered by this job

Generous equity grant

Full healthcare coverage

401k

Unlimited PTO

Job summary

Gallatin AI, Inc. is building ML infrastructure for defense-grade logistics, delivering production-ready AI in air-gapped and secure environments. The team focuses on retrieval-grounded systems, data pipelines, and robust evaluation.

This role owns end-to-end ML infrastructure from training to deployment, including CI/CD, reproducibility, and observability, with both on-prem and cloud workloads and an emphasis on restricted-network deployments.

Qualifications

• 5+ years in MLOps, ML platform, or infrastructure engineering.
• Strong Python skills and production-grade code in a real project.
• Deep Kubernetes and containerization experience, plus infrastructure as code.
• Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent).
• Hands-on GPU infrastructure experience: scheduling, memory sizing, and cost.
• Hands-on experience deploying production software in IL5/IL6 environments, including restricted-network deployments.

Responsibilities

• Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware.
• Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing.
• Build for DDIL: local inference with configurable fallback and sane resource envelopes.
• Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works.
• Own infrastructure as code, containerization, and GitOps deployment across environments from dev to disconnected enclave.
• Make reproducibility a hard requirement: reproducible results from commit and dataset version.
• Build the evaluation harness and observability: regression suites, LLM-as-judge pipelines, and defensible metrics.
• Own ingestion, versioning, and lineage for logistics and doctrinal data; build embedding pipelines and review queues.
• Deploy and operate ML systems in IL5/IL6 environments, including air-gapped enclaves and security controls.

Skills

Python

Kubernetes

Infrastructure as code

AWS SageMaker

GPU infrastructure

GitOps

CI/CD

Education

CS degree or equivalent

Tools

SageMaker

EKS

GitOps tooling

ArgoCD

vLLM

TensorRT-LLM

Job description

Gallatin AI, Inc. is building ML infrastructure for defense-grade logistics, delivering production-ready AI in air-gapped and secure environments. The team focuses on retrieval-grounded systems, data pipelines, and robust evaluation.

This role owns end-to-end ML infrastructure from training to deployment, including CI/CD, reproducibility, and observability, with both on-prem and cloud workloads and an emphasis on restricted-network deployments.