Job Summary

Join our dynamic customer's team as a hands-on MLOps Engineer. You will be instrumental in designing, deploying, and automating comprehensive machine learning pipelines. By leveraging your expertise in AWS services and established MLOps best practices, you will help operationalize advanced ML solutions within a fast-paced, collaborative environment. This role is perfect for dedicated professionals who value clear communication and are passionate about building impactful ML systems.

Key Responsibilities

• Design, develop, and maintain robust ML pipelines for scalable production deployment.
• Implement and manage CI/CD workflows tailored for machine learning code and artifacts.
• Utilize core AWS services, with a strong emphasis 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 to ensure reliability and reproducibility.
• Collaborate closely with data scientists, engineers, and stakeholders to optimize ML models and deployment strategies.
• Monitor, troubleshoot, and enhance ML systems for peak performance, availability, and scalability.
• Maintain clear, concise, and comprehensive documentation for all 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, particularly EKS, ECS, ECR, SageMaker, Step Functions, EventBridge, SNS/SQS, and Model Registry.
• Deep understanding of core ML concepts and the intricacies of deploying ML code in production-grade systems.
• Strong experience with MLFlow for experiment tracking and model management.
• Solid grasp of CI/CD concepts adapted for machine learning workflows.
• Exceptional written and verbal communication skills, with a strong focus 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.