Ml Ops Lead

Role Name: ML Ops Lead

Location: Burbank, CA (Remote)

Duration: 12 month

We are looking for an experienced MLOps Lead Engineer to design, build, and lead scalable machine learning platforms and pipelines. The ideal candidate will have strong expertise in deploying, monitoring, and managing ML models in production, with hands-on experience in AWS and modern MLOps tools. You will lead a team of MLOps/ML engineers and define best practices for ML lifecycle management.

Key Responsibilities:

• Lead the design and implementation of end-to-end MLOps architecture and strategy.
• Build and maintain scalable ML pipelines for training, testing, deployment, and monitoring.
• Deploy and manage ML models in production using AWS services such as SageMaker, EKS, S3, Lambda, and CloudWatch.
• Establish CI/CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or GitLab.
• Implement model monitoring, drift detection, and performance tracking.
• Collaborate with Data Scientists and Data Engineers to productionize ML models.
• Define MLOps best practices, coding standards, and automation frameworks.
• Lead and mentor a team of engineers, ensuring timely delivery and high-quality outcomes.
• Ensure security, scalability, and cost optimization of ML infrastructure.
• Drive cloud-native and container-based solutions using Docker and Kubernetes.

Required Skills & Qualifications:

• 8+ years of IT experience with 4+ years in MLOps/ML Engineering roles.
• 2+ years of experience in a technical leadership or lead role.
• Strong experience with AWS (SageMaker, EC2, S3, EKS, Lambda, IAM, CloudWatch).
• Hands-on experience with CI/CD pipelines for ML workflows.
• Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience with Docker, Kubernetes, and container orchestration.
• Strong understanding of ML lifecycle management and model governance.
• Experience with monitoring tools for model performance and data drift.
• Knowledge of infrastructure as code (Terraform, CloudFormation preferred).