CI/CD Engineer – Machine Learning

Industry & Sector:

• Design, build, and operate end-to-end MLOps pipelines on Databricks: data ingestion, model training, model registry, and production deployment.
• Author and maintain CI/CD pipelines for ML code and infrastructure using Jenkins, Bitbucket, and automation IaC patterns to enable repeatable, auditable releases.
• Integrate code-quality and security gates using SonarQube and enforce branching and release strategies in Bitbucket for collaborative delivery.
• Manage model artifact storage, versioning, and lifecycle on AWS (S3) and Databricks Model Registry; automate promotion from staging to production.
• Operationalize model monitoring and alerting—metric collection, drift detection, and automated retraining triggers to ensure SLA-driven model reliability.
• Troubleshoot operational issues, optimize performance of Spark/Databricks jobs, and collaborate closely with Data Science and Data Engineering to productionize models.
  

Skills & Qualifications

• 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).
  

Qualifications

• Bachelor's degree in Computer Science, IT, Engineering or equivalent practical experience.
• Proven experience delivering production ML systems and CI/CD for ML in a cloud environment (AWS preferred).
• Strong understanding of ML lifecycle, model governance, observability, and reproducible training pipelines.
  

• Fast-paced, collaborative engineering culture with strong emphasis on automation, observability and engineering excellence.
• Opportunity to work on large-scale Databricks/Spark workloads and shape MLOps best practices across product lines.
• Competitive compensation, upskilling budget, and flexible hybrid work options (role-dependent).