Description

Own the MLOps architecture, automation, and operationalization of machine learning models and data science projects.

This role is on-site.

Responsibilities

• Research, implement, and maintain MLOps tools and frameworks to raise organizational maturity.
• Manage end-to-end pipelines for model deployment, inference, monitoring, and retraining.
• Implement drift detection for data and model performance monitoring.
• Develop and publish REST APIs for ML model integration and maintain experiment tracking.
• Conduct internal training on MLOps tools and drive agile, automated approaches for data science.

Required Skills

• 5+ years of experience in Kubernetes and operationalizing data science projects.
• Hands-on experience with MLOps frameworks: MLFlow, KubeFlow, AirFlow.
• Proficiency in Python for ML and automation tasks.
• Experience with cloud ML platforms: AWS SageMaker, Azure ML Studio, or GCP Vertex AI.
• Infrastructure as Code experience with Terraform.
• Data processing skills with PySpark and Azure Databricks.

Preferred Skills

• Experience with CI/CD/CT pipelines, GitHub Actions, and AWS CodePipeline.
• Familiarity with AKS, Fast API, and Unix command line toolkit.
• Exposure to DataRobot or DKube platforms.

Key Skills

Education

Any Graduate