MLOps Engineer — Databricks

We are looking for a skilled Databricks MLOps Developer to design, develop, and maintain machine learning operations workflows on the Databricks Lakehouse platform. This role involves building scalable pipelines, automating ML lifecycle processes, and ensuring robust deployment and monitoring of models in production. Key Responsibilities Develop MLOps Pipelines - Implement automated workflows for ML model training, deployment, and monitoring using Databricks, MLflow, and Delta Lake. Model Lifecycle Management - Manage models through development, staging, and production environments using MLflow Model Registry. CI/CD Integration - Build and maintain CI/CD pipelines for ML projects using Git, Azure DevOps, or similar tools. Data Preparation & Feature Engineering - Collaborate with data engineers and scientists to prepare datasets and optimize feature pipelines on Databricks. Monitoring & Governance - Set up monitoring for model performance, data drift, and compliance using Databricks and cloud-native tools. Documentation & Collaboration - Document workflows and best practices; work closely with cross-functional teams to deliver ML solutions. Required Skills & Qualifications - Hands-on experience with Databricks, MLflow, and Delta Lake. - Strong programming skills in Python and SQL. - Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn). - Experience with CI/CD tools (Azure DevOps, Jenkins, GitHub Actions). - Knowledge of cloud platforms (AWS, Azure, or GCP). - Understanding of containerization (Docker) and orchestration (Kubernetes). Preferred Qualifications - Databricks certification (Machine Learning Professional or Data Engineer). - Experience with Unity Catalog for data governance. - Exposure to feature store concepts and ML observability tools. Soft Skills - Strong problem-solving and analytical skills. - Excellent communication and teamwork abilities.