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Job Description & How to Apply Below
Join our customer's team as a hands-on MLOps Engineer, where you'll play a pivotal role in shaping, deploying, and automating end-to-end machine learning pipelines. Leveraging your expertise in AWS services and MLOps best practices, you will help operationalize cutting-edge ML solutions in a fast-paced, collaborative environment. This opportunity is ideal for passionate professionals who care deeply about clear communication and impactful ML systems.
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, Sage Maker (including processing, training, batch transform, hyperparameter tuning), Step Functions, Event Bridge, SNS/SQS, and Sage Maker 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, Sage Maker, Step Functions, Event Bridge, 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.
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