MLOPS Engineer

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MLOps Engineer

Experience Level: 5 - 8 Years Location: Chennai

About the Role: We are looking for a seasoned MLOps Engineer to establish and optimize our machine learning operations within the U.S. healthcare payer business. This role is crucial for bridging the gap between ML model development and production, ensuring seamless deployment, monitoring, and maintenance of our AI/ML solutions at scale. You will be responsible for building robust, automated pipelines that enable rapid iteration and reliable performance of our critical healthcare-focused models.

Responsibilities:

• Design, build, and maintain end-to-end MLOps pipelines for machine learning models, from experimentation to production deployment and monitoring.
  
• Implement and manage CI/CD strategies specifically tailored for ML workflows, ensuring automated testing, deployment, and versioning of models and code.
  
• Containerize ML models and applications using Docker for consistent and reproducible deployments across various environments.
  
• Automate infrastructure provisioning and management on AWS or Azure using Infrastructure-as-Code (IaC) principles.
  
• Develop and implement monitoring strategies for deployed ML models, tracking performance metrics, data drift, model drift, and ensuring overall model health.
  
• Establish alerting mechanisms and incident response procedures for ML systems.
  
• Collaborate closely with ML Engineers and Data Scientists to operationalize their models, providing guidance on best practices for model development, packaging, and deployment.
  
• Manage and optimize ML model registries, versioning, and artifact management.
  
• Ensure compliance with healthcare data security and privacy regulations (e.g., HIPAA) across the MLOps lifecycle.
  
• Research and evaluate new MLOps tools and technologies to improve efficiency, scalability, and reliability of ML systems.

Required Technical Skills:

• 
  Programming Language: Strong proficiency in Python for scripting and automation.
  
• Machine Learning Libraries (familiarity): Understanding of PyTorch and scikit-learn to interact with ML models.
  
• Version Control: Expert with Git and GitHub for code and model versioning.
  
• Testing: Experience with Python unittest framework and Pytest for automating tests across the ML pipeline (unit, integration, API, model validation tests).
  
• Deployment: Extensive experience with Docker for containerization and deploying applications on AWS or Azure.
  
• Cloud Platforms: Hands-on experience with AWS or Azure cloud services for ML and MLOps (e.g., AWS Sagemaker, Lambda, EC2, ECS, EKS, Azure ML, Azure Kubernetes Service).
  
• CI/CD: Deep expertise in setting up and managing CI/CD pipelines using AWS CodePipeline or similar tools (e.g., Jenkins, GitLab CI, Azure DevOps).
  
• Orchestration: Experience with orchestration tools (e.g., Kubernetes, Airflow) for managing complex ML workflows.
  
• Monitoring Tools: Familiarity with monitoring tools for ML model performance (e.g., Prometheus, Grafana, custom dashboards).