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ML Engineer CONTRCAT - Employe Hub
ML Engineer
ML Engineer responsible for end‑to‑end model deployment, monitoring, and retraining in production, ensuring low latency and high stability for plant control systems using Python, MLOps tools, CI/CD pipelines, Docker, and Kubernetes.
About the role
Key Responsibilities
- Build and maintain CI/CD pipelines for ML workflows, from notebook to production.
- Package, deploy, and serve models in batch, real‑time, and edge/on‑prem environments.
- Set up and manage MLOps infrastructure: experiment tracking, model registry, and feature pipelines.
- Monitor model performance, trigger automated retraining, and ensure system stability and low latency for plant control integration.
- Collaborate with data scientists and plant engineers to translate research prototypes into reliable production systems.
Requirements
- Strong experience with Python and ML libraries (scikit‑learn, TensorFlow, PyTorch).
- Hands‑on expertise in MLOps tools (MLflow, DVC, SageMaker, etc.) and CI/CD platforms (GitHub Actions, Jenkins).
- Proficiency with containerization (Docker) and orchestration (Kubernetes) for scalable deployments.
- Knowledge of cloud services (AWS, GCP, or Azure) and on‑prem infrastructure.
- Excellent problem‑solving skills and ability to work in a fast‑paced, safety‑critical environment.
Skills
pythonmachine learningmlopscicddockerkubernetes