Expert MLOps Engineer (REF5185C)

TLDR

Operationalize complex ML models for Defense applications with a focus on automation, observability, and lifecycle management in a zero downtime environment.

You will bridge the gap between Data Science and Operations. Your goal is to operationalize complex ML models for Defense applications, ensuring that AI-driven insights are delivered to the field with "Zero-Downtime" reliability and full traceability.

Key Responsibilities:

End-to-End Lifecycle: Build and manage the full ML lifecycle—from experiment tracking to model deployment and retraining.
Containerization: Master the deployment of ML workloads using Docker and Kubernetes (OpenShift).
Automation: Implement ML-specific CI/CD (e.g., CML, Kubeflow Pipelines) to automate the promotion of models to production.
Observability: Set up specialized monitoring for model drift, data quality, and prediction accuracy.
Scalability: Architect distributed systems for large-scale model inference.

Requirements:

Experience: Senior-level experience in MLOps or Production ML.
Tech Stack: Strong Python, ML Frameworks (PyTorch/TensorFlow), and MLOps tools (MLflow, Kubeflow, or SageMaker).
IaC: Experience with Terraform and Cloud optimization.
Defense Readiness: EU Citizenship is mandatory.
Language: Fluent English; German is a strong plus.

* Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation.