MLOps Engineer – Databricks

Job Summary

We are seeking a skilled MLOps Engineer with hands-on experience in Databricks and CI/CD automation to support the deployment and operationalization of machine learning models. The ideal candidate should possess a strong understanding of AWS services, Jenkins, SonarQube, and Bitbucket, along with basic proficiency in Python and a good grasp of the ML lifecycle.

Key Responsibilities

• Manage and support end-to-end MLOps workflows for model development, deployment, and monitoring.

• Work extensively on Databricks for building and managing ML pipelines, data workflows, and model execution.

• Design and maintain CI/CD pipelines using Jenkins to automate ML model deployments.

• Integrate SonarQube for code quality checks and ensure bug-free ML and data pipeline code.

• Manage AWS services, including S3 buckets, for model storage, versioning, and artifact management.

• Collaborate closely with Data Science and Data Engineering teams to ensure smooth transitions of models from development to production.

• Utilize Bitbucket for version control, branching strategies, and collaborative code development.

• Review and modify deployment scripts or configurations to enhance reliability and performance.

• Participate in troubleshooting, debugging, and continuous improvement of MLOps processes.

• Ensure adherence to best practices in code quality, automation, and deployment governance.

Required Skills

• Databricks (Mandatory): Strong hands-on experience with data pipelines, model training, and deployment workflows.

• Jenkins: Practical knowledge of CI/CD pipeline setup, configuration, and maintenance.

• AWS (S3 Buckets): Experience managing model artifacts, datasets, and configurations.

• SonarQube: Understanding of code quality metrics and best practices for bug fixing.

• Bitbucket (Important): Proficiency in version control and branching strategies for collaborative projects.

• Python (Optional): Basic understanding for reading and modifying ML-related scripts.

• Modification Understanding: Ability to analyze and adapt existing workflows, pipelines, or configurations as per project needs.

Good to Have

• Familiarity with ML lifecycle management tools such as MLflow.

• Understanding of containerization technologies (e.g., Docker, Kubernetes) for ML model deployment.

• Experience working in cloud-based MLOps environments (AWS / Azure).

Education

• Bachelor’s degree in Computer Science, Information Technology, or a related field.