MLOps Engineer

Job Summary:

MLOps Engineer

Key Responsibilities:

• Design and implement CI/CD pipelines for machine learning workflows.
  
• Automate the deployment of machine learning models into production environments.
  
• Develop infrastructure and tooling for model versioning, tracking, monitoring, and rollback (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
  
• Work closely with data scientists to productionize notebooks and scripts.
  
• Ensure reproducibility, scalability, and security of ML systems.
  
• Manage cloud infrastructure (e.g., AWS, GCP, Azure) and container orchestration platforms like Kubernetes or Docker Swarm.
  
• Monitor deployed models for performance degradation, data drift, and errors.

Preferred Qualifications:

• Experience working with model registries and feature stores.
  
• Knowledge of data versioning and data validation practices.
  
• Familiarity with deep learning frameworks (TensorFlow, PyTorch) and ML libraries (scikit-learn, XGBoost).
  
• Understanding of software development practices, testing, and version control (Git).
  
• Knowledge of security and compliance best practices in ML systems.
  
• Previous experience working in agile/scrum teams.