MLOps Engineer

About the Project:

This is an exciting opportunity to join our team in building a robust MLOps framework for a critical machine learning project. We have a successfully developed ML model in Databricks, and now we need your expertise to operationalize it effectively.

Responsibilities:

• Design and implement a production-ready repository for our ML models using infrastructure as code principles.
• Develop and maintain CI/CD pipelines for seamless model deployment and updates.
• Customize and optimize ML pipelines for specific use cases within our Databricks environment.
• Ensure model governance and compliance by leveraging Databricks Model Registry and MLflow.
• Collaborate with data scientists and engineers to optimize model performance and address any operational challenges.
• Document and maintain all MLOps processes and procedures.

Required Skills:

• Strong experience in MLOps principles and best practices.
• Proficiency in Python and experience with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Expertise in working with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Solid understanding of CI/CD pipelines and tools (e.g., Jenkins, GitLab CI/CD, Azure DevOps).
• Experience with version control systems (e.g., Git).
• Strong understanding of data engineering principles and best practices.
• Experience with Databricks and MLflow is highly desirable.

Preferred Skills:

• Experience with infrastructure as code tools (e.g., Terraform, Ansible).
• Knowledge of cloud-native technologies and serverless computing.
• Experience with data governance and compliance regulations.