MLOps Engineer

We’re looking for a skilled MLOps Engineer to build the bridges between data science, engineering, and DevOps — ensuring smooth deployment, monitoring, and scaling of AI/ML models used in personalized learning, recommendation, and analytics systems.

Responsibilities

• Implement CI/CD pipelines for ML models using Azure ML, MLflow, or Kubeflow.
• Containerize and deploy models as APIs or microservices.
• Automate model retraining and evaluation based on user interaction data.
• Develop monitoring dashboards (Prometheus/Grafana) for model performance and drift detection.
• Manage feature stores, model registries, and experiment tracking.
• Collaborate with Data & AI teams to standardize data schemas and versioning.
• Ensure security, compliance, and reproducibility across ML pipelines.

Requirements

• 3+ years in MLOps, DevOps, or DataOps roles.
• Proficient in Python, Docker, Kubernetes, and CI/CD tools (GitHub Actions, Azure DevOps).
• Strong understanding of ML lifecycle management (training, deployment, monitoring).
• Experience with MLflow, DVC, or Kubeflow Pipelines.
• Familiarity with cloud infrastructure (Azure/AWS/GCP) and IaC tools (Terraform, Bicep).
• Understanding of data governance and GDPR compliance.