Senior MLOps Engineer — Hybrid Berlin AI Infra

ICEYE; Berlin (NH); Hybrid; USD 134,000 - 179,000; Full time

Job summary

ICEYE, a global leader in sovereign intelligence from space, seeks a Senior MLOps Engineer for its ISR Analytics team in Berlin. You will build infrastructure and deployment practices for AI models, both in the cloud and on-prem, with focus on reproducibility and auditability.

You will work closely with data scientists and software engineers to ship robust ML workflows. The role requires hands-on experience deploying ML models, strong Python (PyTorch), and expertise with Kubernetes and GPU

Qualifications

• Senior hands-on engineer (not a manager or architect).
• Proven experience deploying ML models to production.
• Strong Python, reading ML code (PyTorch or similar).
• Hands-on Kubernetes and containers with GPU workloads.
• Infrastructure as code and CI/CD for ML (Terraform, Helm, GitHub Actions).
• Daily use of AI tools in engineering work, beyond chat.
• High autonomy: identify problems, propose fixes, drive them through.

Responsibilities

• Build training, evaluation and packaging pipelines from experiment to release.
• Deploy and serve models on Kubernetes with GPUs, in cloud and on-prem, including air-gapped sites.
• Package models, weights and dependencies for offline install.
• Manage GPU compute: scheduling, drivers, utilization and cost across cloud and on-prem.
• Track data, experiments and model lineage for reproducibility and auditability.
• Monitor production models for performance, drift and failures without external connectivity.
• Write Python tooling used by the AI team and work in the model codebase.

Skills

Python

ML deployment

PyTorch

Kubernetes

Cloud/On-prem

CI/CD for ML

Autonomy

Air-gapped/secure environments

Tools

Kubernetes

Terraform

Helm

GitHub Actions

MLflow

Kubeflow

Job description

ICEYE, a global leader in sovereign intelligence from space, seeks a Senior MLOps Engineer for its ISR Analytics team in Berlin. You will build infrastructure and deployment practices for AI models, both in the cloud and on-prem, with focus on reproducibility and auditability.

You will work closely with data scientists and software engineers to ship robust ML workflows. The role requires hands-on experience deploying ML models, strong Python (PyTorch), and expertise with Kubernetes and GPU