MLOps Engineer

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Job Summary

Join our customer's dynamic team as a hands-on MLOps Engineer and play a pivotal role in driving the development, deployment, and automation of robust machine learning pipelines. Utilize your expertise in AWS and MLOps to help architect, optimize, and scale production-ready ML solutions across diverse projects. We value professionals who excel in both written and verbal communication, collaborating effectively in a high-performing environment.

Key Responsibilities

• Design, automate, and maintain end-to-end ML pipelines for model training, deployment, and monitoring on AWS infrastructure.
• Lead the development and operationalization of machine learning solutions using AWS services such as EKS, ECS, ECR, SageMaker, Step Functions, EventBridge, SNS/SQS, and Model Registry.
• Integrate ML Flow to manage experiment tracking, model versioning, and lifecycle management.
• Implement and manage CI/CD pipelines specifically tailored for machine learning code and workflows.
• Collaborate closely with data scientists, engineers, and stakeholders to productionize ML models and ensure reliability, scalability, and security.
• Monitor and troubleshoot ML systems in production, proactively resolving issues and optimizing performance.
• Document workflows, processes, and architectural decisions with clarity and precision.

Required Skills and Qualifications

• Proven experience in MLOps with hands-on expertise in designing and deploying ML pipelines in production environments.
• Strong proficiency with AWS core services, especially EKS, ECS, ECR, SageMaker (jobs, batch transform, hyperparameter tuning), Step Functions, EventBridge, SNS/SQS, and Model Registry.
• Solid understanding of core machine learning concepts and best practices for productionizing ML code.
• Demonstrated experience with ML Flow for managing model lifecycle and experiment tracking.
• Expertise in implementing CI/CD pipelines for ML projects.
• Excellent written and verbal communication skills, with a collaborative mindset.
• A passion for automation, optimization, and scalable system design.

Preferred Qualifications

• Experience supporting large-scale, distributed machine learning systems in a cloud environment.
• Familiarity with container orchestration and monitoring tools within AWS.
• Contributions to open-source MLOps or ML engineering projects.