Distributed Cloud | MLOps

We are looking for an MLOps Engineer to bridge the gap between model development and production. Your mission is to build the infrastructure and automated pipelines that allow our AI/ML models to be deployed, monitored, and scaled with high reliability. In this role, you will: Design and implement automated end-to-end pipelines (CI/CD/CT) for machine learning workflows. Standardize the way models are packaged, versioned, and deployed (Containerization & 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 using IaC principles. Work closely with Data Scientists to transition experimental code into robust, production-ready microservices. Ensure the cost-efficiency and latency optimization of model serving layers. 3+ years of experience in MLOps, DevOps, or Software Engineering with a strong focus on ML delivery. Proficiency in Python and experience with ML orchestration tools (e.g., Kubeflow, MLflow, or Airflow). Deep knowledge of Docker and Kubernetes for managing distributed ML workloads. Hands-on experience with automation tools (GitLab CI, GitHub Actions, or Jenkins). Experience with ML services in AWS (SageMaker), GCP (Vertex AI), or Azure (Azure ML). Familiarity with data versioning (DVC) and feature stores is a strong plus. Experience with observability tools like Prometheus, Grafana, or specialized ML monitoring platforms. Professional level of English (written and spoken). Devoteam Group works for equal opportunities, promoting its employees based on merit, and actively fights against all forms of discrimination. We are convinced that diversity contributes to the creativity, dynamism, and excellence of our organization. All of our vacancies are open to people with disabilities.