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ICEYE is the world leader in sovereign intelligence from space, operating the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. The Intelligence, Surveillance and Reconnaissance (ISR) Analytics team builds AI models that turn satellite imagery into intelligence for defense customers.
As a Senior MLOps Engineer, you will work with data scientists to build sustainable infrastructure, workflows, and deployment practices for AI models in both cloud and on-premise environments. You will sit with the AI team to build the infrastructure they use to train, evaluate, and ship models. Every environment must be temporary and reproducible from code; models are only complete when running and monitored on hardware outside your control.
Your day-to-day responsibilities include:
- Building training, evaluation, and packaging pipelines that move models from experiment to versioned, deployable releases
- Deploying and serving models on Kubernetes with GPUs in cloud and on-prem environments, including air-gapped sites
- Packaging models, weights, and dependencies for offline installation and upgrades
- Managing GPU compute across cloud and on-prem hardware: scheduling, drivers, utilization, and cost
- Tracking data, experiments, and model lineage for reproducibility and auditability
- Monitoring models in production for performance, drift, and failures without relying on outside connectivity
- Writing Python tooling for the AI team and working in the model codebase alongside them
This is a hands-on senior individual contributor role reporting to the VP of ISR Analytics. You will have high autonomy to identify problems, propose solutions, and drive them through.
REQUIREMENTS:
Must haves:
- Senior hands-on engineer with proven experience deploying ML models to production and maintaining them (not just training)
- Strong Python proficiency and comfort reading and modifying ML code (PyTorch or similar)
- Hands-on Kubernetes and containers experience, including GPU workloads
- Infrastructure as code and CI/CD for ML (e.g., Terraform, Helm, GitHub Actions)
- Daily use of AI tools in engineering work beyond chat (code generation, review, configuration, testing)
- High autonomy: finding problems, proposing fixes, and driving them through
- Motivation to work in defense technology
Nice to haves:
- Experience deploying models to on-prem, edge, or air-gapped environments
- Model serving and optimization tools (Triton, TorchServe, KServe, ONNX, TensorRT, quantization)
- Computer vision or geospatial data experience (satellite, SAR, raster, PostGIS)
- ML pipeline and tracking tools (MLflow, Kubeflow, Argo Workflows, DVC, Weights & Biases)
- Large-scale data processing for imagery (Dask, Ray, Spark, object storage)
- Strong individual contributor track record in startups or fast-changing companies
- Clear communication skills working with researchers and experienced engineers
