Title: MLOps Engineer
Duration: Contract
Work Location: Santa Clara, CA – onsite
MLOps Engineer with hands-on experience in Databricks to join the AI/ML team. You will be responsible for designing, building, and maintaining scalable and reliable machine learning infrastructure and pipelines. Your work will enable data scientists and ML engineers to develop, deploy, and monitor models efficiently in production environments.
Key Responsibilities:
• Design and implement CI/CD pipelines for ML workflows using Databricks and other tools.
• Automate model training, validation, deployment, and monitoring processes.
• Collaborate with data scientists to productionize ML models and ensure reproducibility.
• Manage and optimize Databricks clusters, jobs, and workflows.
• Implement model versioning, experiment tracking, and performance monitoring.
• Ensure compliance with data governance, security, and privacy standards.
• Troubleshoot and resolve issues in ML pipelines and infrastructure.
• Contribute to the development of internal MLOps best practices and documentation.
Required Qualifications:
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 8+ years of experience with strong MLOps skills or with ML engineering roles
• Strong experience with Databricks, including notebooks, jobs, Delta Lake, and MLflow.
• Proficiency in Python and familiarity with ML libraries ( TensorFlow, PyTorch).
• Experience with Azure cloud platform
• Familiarity with containerization (Docker) and orchestration (Kubernetes) is a plus.
• Experience with CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps).
• Strong understanding of data pipelines, ETL processes, and data engineering principles.
• Databricks certification (e.g., Databricks Certified Machine Learning Professional).
• Experience with feature stores, model registries, and monitoring tools.
• Knowledge of Spark and distributed computing concepts.
• Familiarity with infrastructure-as-code tools (e.g., Terraform).
