Senior MLOps Engineer, ISR

• Location: Berlin, Germany

• Department: ISR Analytics

• Reports to: VP of ISR Analytics

• Employment type: Permanent

• Workplace model: Hybrid

• Employment is subject to applicable security screening (incl. SUPO, where required)

ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.

ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.

ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.

Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.

Your day-to-day 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.

• 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.

• Outline the stages of this role, including task stages.