MLOps Engineer

Job highlights

• 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,

Job Title: MLOps Engineer
Job Type: Contractor
Location: On-site Gurugram, Pune or Bangalore
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,