Description

You will build the foundation of our MLOps capabilities by managing the end-to-end machine learning lifecycle.

Responsibilities

• Design, implement, and manage CI/CD pipelines tailored specifically for machine learning workflows.
• Automate the training, validation, and deployment of models across production, staging, and testing environments.
• Develop and maintain the infrastructure and tools required to monitor ML algorithms in production.
• Monitor, debug, and optimize MLOps pipelines at the module, pipeline, and system levels.
• Collaborate with data scientists and engineers to optimize automation and implement scalable ML operations.

Required Skills

• 10+ years of Machine Learning experience.
• Minimum of 5 years of experience setting up and maintaining CI/CD pipelines.
• Proficiency in Python, Bash, or Shell for automation tasks.
• Hands-on experience with Jenkins, GitLab CI, or Azure DevOps.
• Strong understanding of containerization and orchestration using Docker and Kubernetes.
• Familiarity with machine learning frameworks including TensorFlow, PyTorch, or Scikit-learn.
• Experience managing machine learning model versioning and data validation processes.

Preferred Skills

• Experience with ML training data lifecycles and data storage engines including SQL, NoSQL, Elasticsearch, or Timeseries databases.
• Knowledge of distributed computer systems and web software development.

Key Skills

Education

Any graduate