MLOps Engineer II

About the role

We are seeking an MLOps Engineer II to independently design, implement, and manage automation of the machine learning lifecycle. The role bridges DevOps and data science, ensuring reliable, efficient, and scalable model deployment and monitoring in production environments.

Key responsibilities

• Design and implement CI/CD pipelines for machine learning models.
• Automate model training, validation, testing, and deployment using appropriate tools.
• Develop and maintain automated testing frameworks for ML models.
• Manage deployment strategies such as A/B testing and canary releases.
• Build and maintain scalable infrastructure for model serving with Kubernetes or serverless functions.
• Implement model versioning, rollback mechanisms, and optimize serving for latency and throughput.
• Set up comprehensive monitoring, logging, and alerting to detect model and data drift.
• Contribute to model governance policies and ensure security and privacy compliance.
• Collaborate with data scientists and software engineers to integrate models into applications and services.

Required profile

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in DevOps, software engineering, or related roles with a focus on ML deployment.
• Strong ownership, accountability, and excellent communication skills.
• Effective time management, analytical thinking, and ability to work collaboratively.

Required skills

• Jenkins or GitLab CI
• Docker
• Kubernetes
• AWS, Azure, or GCP
• MLflow or Kubeflow
• TensorFlow, PyTorch, scikit-learn
• Python and Bash scripting
• Terraform or CloudFormation