Senior ML Ops Engineer

Key Responsibilities

• Design, develop, and maintain  end-to-end MLOps pipelines  to automate the machine learning lifecycle from training to deployment and monitoring.
• Collaborate with data scientists, ML engineers, and platform teams to  operationalize ML models  across cloud and hybrid environments.
• Build and manage  containerized environments  for training and inference using Docker and Kubernetes.
• Implement  CI / CD workflows  (e.g., GitHub Actions, Jenkins) for deploying ML models.
• Ensure  observability and monitoring  of models in production (latency, drift, performance, errors).
• Support model deployment to a variety of targets including APIs, applications, dashboards, and edge devices.
• Implement  model versioning , rollback strategies, governance, and traceability using tools like MLflow or Kubeflow.
• Drive best practices across teams and provide technical mentorship on MLOps topics.
• Continuously evaluate and integrate new tools and technologies to improve MLOps capabilities.

Required Skills & Qualifications

Preferred Qualifications

Skills Required

Gcp, Aws, Airflow, Jenkins