Description

You will build and maintain end-to-end ML pipelines to support the training, deployment, and monitoring of machine learning models.

Responsibilities

• Design and productionize machine learning models into scalable, containerized production systems.
• Build CI/CD pipelines for ML workflows, including automated training, testing, and deployment.
• Automate infrastructure provisioning and environment management for ML workloads.
• Implement monitoring for model performance, data drift, and system health.
• Enforce repeatability and traceability for ML experiments and model deployments.
• Partner with Data Scientists and Platform Engineers to standardize feature engineering and model promotion.

Required Skills

• 5+ years of experience in software, data, or platform engineering.
• 3+ years of hands-on experience in MLOps or ML platform engineering.
• Proficiency in Python.
• Experience with MLflow and Kubeflow.
• Hands-on with Docker and Kubernetes.
• Cloud experience across AWS, Azure, or GCP.
• Experience implementing CI/CD and Terraform.
• Proven track record supporting production-grade machine learning systems.
• Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent experience.

Preferred Skills

• Experience with scikit-learn, TensorFlow, or PyTorch.
• Knowledge of feature stores like Feast and workflow orchestration tools such as Airflow, Dagster, or Prefect.
• Familiarity with Spark, SQL, and data warehousing fundamentals.

Key Skills

Education

Any Graduate