MLOps Engineer – Distributed Cloud

About the role

We are looking for an MLOps Engineer to bridge the gap between model development and production, building infrastructure and automated pipelines for AI/ML models.

Key responsibilities

• Design and implement automated end‑to‑end pipelines (CI/CD/CT) for machine‑learning workflows.
• Standardize model packaging, versioning, and deployment using containerization and orchestration.
• Build monitoring systems to track model performance, data drift, and system health in production.
• Manage scalable compute and storage resources for training and inference with Infrastructure‑as‑Code principles.
• Collaborate closely with Data Scientists to turn experimental code into robust, production‑ready micro‑services.
• Ensure cost‑efficiency and latency optimisation of model‑serving layers.

Required profile

• 3+ years of experience in MLOps, DevOps, or Software Engineering focused on ML delivery.
• Proficiency in Python and experience with ML orchestration tools such as Kubeflow, MLflow, or Airflow.
• Deep knowledge of Docker and Kubernetes for managing distributed ML workloads.
• Hands‑on experience with automation tools like GitLab CI, GitHub Actions, or Jenkins.
• Experience with cloud ML services (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Professional level of English, both written and spoken.

Required skills

• Python
• Kubeflow
• MLflow
• Airflow
• Docker
• Kubernetes
• GitLab CI
• GitHub Actions
• Jenkins
• AWS SageMaker
• GCP Vertex AI
• Azure ML
• DVC (data versioning)
• Prometheus
• Grafana