MLOPs Engineer

Key Responsibilities

• Design, build, and manage end-to-end ML pipelines
• Deploy and maintain machine learning models in production
• Implement CI/CD pipelines for ML workflows
• Automate model training, versioning, and deployment processes
• Monitor model performance, data drift, and system health
• Manage containerized deployments using Docker and Kubernetes
• Collaborate with cross-functional teams to improve ML delivery
• Ensure best practices in scalability, security, and reliability
  
  

Required Skills & Qualifications

• 2–5 years of experience in MLOps, DevOps, or ML Engineering
• Strong proficiency in Python
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with Docker and Kubernetes
• Knowledge of CI/CD tools (Jenkins, GitHub Actions, GitLab CI, etc.)
• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform)
• Understanding of ML lifecycle, deployment, and monitoring