Key Responsibilities

• Design, develop, and maintain robust ML pipelines for scalable deployment in production environments.
• Implement and manage CI/CD workflows specific to machine learning code and artifacts.
• Utilize AWS core services, with a strong focus on EKS, ECS, ECR, SageMaker (including processing, training, batch transform, hyperparameter tuning), Step Functions, EventBridge, SNS/SQS, and SageMaker Model Registry.
• Automate and orchestrate machine learning workflows, ensuring reliability and reproducibility.
• Collaborate with data scientists, engineers, and stakeholders to optimize ML models and deployment strategies.
• Monitor, troubleshoot, and enhance ML systems for optimal performance, availability, and scalability.
• Maintain clear, concise, and comprehensive documentation for pipelines, deployments, and operational processes.

Required Skills and Qualifications

• Proven hands-on experience as an MLOps Engineer or in a similar role supporting live ML applications.
• Expertise in AWS cloud services, especially EKS, ECS, ECR, SageMaker, Step Functions, EventBridge, SNS/SQS, and Model Registry.
• Deep understanding of core ML concepts and the nuances of deploying ML code in production-grade systems.
• Strong experience with MLFlow for experiment tracking and model management.
• Solid grasp of CI/CD concepts tailored to machine learning workflows.
• Exceptional written and verbal communication skills, with a strong emphasis on collaboration and documentation.
• Demonstrated ability to work on-site in Gurugram, Pune, or Bengaluru.

Preferred Qualifications

• Exposure to advanced ML workflow automation and monitoring tools.
• Previous experience in high-performance, large-scale ML environments.
• Relevant certifications in AWS or MLOps.