Description

You will bridge the gap between data science and production by operationalizing machine learning workflows.

Responsibilities

• Build and maintain CI/CD pipelines for ML model development, testing, and deployment.
• Develop reusable tools and frameworks for data processing, model training, validation, and monitoring.
• Collaborate with data scientists to ensure models are scalable, reliable, and reproducible.
• Manage and optimize compute infrastructure, including cloud and on-prem GPU/CPU clusters.
• Implement observability systems to track model performance, drift, and data integrity.
• Ensure governance through model versioning, reproducibility, and auditability.

Required Skills

• 3+ years of experience in ML Engineering, DevOps, or Infrastructure Engineering.
• Proficiency with AWS or Google Cloud Platform.
• Experience with orchestration tools like Kubernetes and Airflow.
• Hands-on use of MLOps frameworks such as MLflow, Kubeflow, or Metaflow.
• Strong Python coding skills.
• Experience with infrastructure-as-code tools including Terraform and Helm.
• Solid understanding of CI/CD practices and monitoring tools like Prometheus, Grafana, or Datadog.

Preferred Skills

• Experience deploying real-time inference services and batch prediction pipelines.
• Familiarity with model explainability, fairness, and responsible AI practices.
• Exposure to feature stores and experiment tracking platforms.

Key Skills

Education

Any Gradute