Senior MLOps Engineer

Key Responsibilities

• Build and manage end -to -end ML pipelines.
  
• Deploy ML models using CI/CD pipelines.
  
• Develop scalable MLOps infrastructure on AWS, Azure, or GCP.
  
• Automate model training, testing, versioning, and deployment.
  
• Monitor model performance, drift, and data quality.
  
• Collaborate with Data Science and Engineering teams.
  
• Manage Docker and Kubernetes -based deployments.
  
• Optimize and troubleshoot production ML systems.
  

Required Skills

• 7+ years of experience in MLOps/ML Engineering/DevOps.
  
• Strong Python programming skills.
  
• Experience with TensorFlow, PyTorch, or Scikit -learn.
  
• Hands -on experience with Docker and Kubernetes.
  
• Experience with AWS, Azure, or GCP.
  
• Knowledge of CI/CD tools (GitHub Actions, GitLab CI, Jenkins, Azure DevOps).
  
• Experience with MLflow, Kubeflow, or Weights & Biases.
  
• Familiarity with ETL pipelines, APIs, Prometheus, Grafana, and ELK.
  
• Strong understanding of software engineering and system design.