Senior MLOps Engineer, ISR

Job summary

This hands-on MLOps role builds sustainable, reproducible infrastructure and deployment practices for ISR AI models that turn satellite imagery into intelligence for defense customers, across cloud and on-premise systems. The engineer develops model training, evaluation, packaging, and monitoring pipelines; deploys GPU workloads on Kubernetes, including at air-gapped sites; manages GPU compute; and writes Python tooling alongside data scientists.

Benefits

Qualifications

• Senior hands-on engineer (not an engineering manager or architect role).
• Proven experience deploying ML models to production and running them there, not only training them.
• Strong Python, and comfort reading and changing ML code (PyTorch or similar).
• Hands-on Kubernetes and containers, including GPU workloads.
• Infrastructure as code and CI/CD for ML (for example Terraform, Helm, GitHub Actions).
• Daily use of AI tools in engineering work, beyond chat: generating and reviewing code, configuration and tests.
• High autonomy: you find problems, propose fixes and drive them through.
• Motivation to work in new defense: building technology used by defense forces.
• Deployed models to on-prem, edge or air-gapped environments.
• Model serving and optimization: Triton, TorchServe, KServe, ONNX, TensorRT, quantization.
• Computer vision or geospatial data (satellite, SAR, raster, PostGIS).
• ML pipeline and tracking tools such as MLflow, Kubeflow, Argo Workflows, DVC or Weights & Biases.
• Large-scale data processing for imagery (Dask, Ray, Spark, object storage).
• Strong IC track record in a startup or a fast-changing company launching new products.
• Clear communicator who works well with researchers and experienced engineers.

Responsibilities

• Build training, evaluation and packaging pipelines that take a model from experiment to versioned, deployable release.
• Deploy and serve models on Kubernetes with GPUs, in the cloud and on-prem, including air-gapped sites.
• Package models, weights and dependencies so they install and upgrade offline.
• Manage GPU compute: scheduling, drivers, utilization and cost, across cloud and on-prem hardware.
• Track data, experiments and model lineage, so any result can be reproduced and audited.
• Monitor models in production for performance, drift and failures, without relying on outside connectivity.
• Write tooling in Python that the AI team uses every day, and work in the model codebase alongside them.

Skills

Languages

Industry

Company size

Security clearance

Relocation