Machine Learning Operations Engineer

About the role

The MLOps Engineer will design, implement, and support the infrastructure and processes needed to deploy, monitor, and maintain machine learning models in production, working closely with data scientists, data engineers, and business stakeholders.

Key responsibilities

• Design, develop, and maintain end‑to‑end MLOps pipelines for model training, validation, deployment, and monitoring.
• Build and manage CI/CD pipelines for reliable, automated model releases, including versioning, experiment tracking, and artifact management.
• Monitor production models for performance, latency, data quality, concept drift and trigger automated retraining workflows.
• Collaborate with data scientists to operationalize predictive models for demand forecasting, buy quantity optimization, inventory planning, pricing and other retail analytics use cases.
• Integrate ML solutions with enterprise data platforms, APIs and downstream business applications while ensuring security, governance and scalability.

Required profile

• Strong experience implementing MLOps frameworks on cloud platforms such as AWS, Azure or Google Cloud.
• Hands‑on experience with containerization (Docker) and orchestration (Kubernetes).
• Proficiency in Python and automation scripting for ML workflows.
• Ability to troubleshoot production AI systems in a fast‑paced retail environment.

Required skills

• AWS, Azure, Google Cloud
• Docker, Kubernetes
• MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
• GitHub Actions, Azure DevOps, Jenkins, GitLab CI
• Airflow, Prefect, Dagster
• Terraform, CloudFormation
• Python