Databricks Machine Learning Engineer

Overview

• We're looking for a hands-on Machine Learning Engineer with strong Databricks experience to help build and productionize machine learning solutions on a modern Lakehouse platform. This person will work closely with Data Engineers, Data Scientists, and business stakeholders to deploy scalable ML solutions that support predictive analytics, forecasting, recommendation systems, and emerging AI use cases.
• This is an engineering-heavy role focused on taking models from development into production while building robust, scalable ML infrastructure.

Responsibilities

• Build and deploy production-grade machine learning models using the Databricks Lakehouse Platform.
• Design scalable ML pipelines for feature engineering, model training, validation, deployment, and monitoring.
• Develop end-to-end workflows using Databricks Workflows, MLflow, and Delta Lake.
• Build feature pipelines and reusable datasets for machine learning applications.
• Deploy and monitor models using Databricks Model Serving and MLflow.
• Collaborate with Data Engineers to optimize data pipelines for machine learning workloads.
• Work with Data Scientists to productionize forecasting, classification, recommendation, NLP, or GenAI models.
• Improve model performance, reliability, scalability, and cost efficiency.
• Implement CI/CD processes for ML deployments.
• Monitor model drift, data quality, and production performance.
• Follow MLOps best practices throughout the model lifecycle.

• Required Skills
• 5+ years of Machine Learning Engineering experience.
• 2+ years building production solutions in Databricks.
• Strong Python development experience.
• Strong SQL skills.
• PySpark / Apache Spark.
• MLflow (experiment tracking, model registry, deployment).
• Databricks Lakehouse architecture.
• Delta Lake.
• Experience deploying ML models into production.
• Experience building scalable feature engineering pipelines.
• Cloud experience in AWS, Azure, or GCP.
• Git and CI/CD pipelines.

• Preferred Experience
• Databricks Mosaic AI
• Unity Catalog
• Feature Store
• Model Serving
• Vector Search
• RAG applications
• LLMs
• LangChain or similar orchestration frameworks
• Time-series forecasting
• Recommendation systems
• Customer analytics
• Demand forecasting
• MLOps best practices

• Tech Stack
• Databricks
• PySpark
• Python
• SQL
• MLflow
• Delta Lake
• Unity Catalog
• Mosaic AI
• Feature Store
• Git
• Docker
• Kubernetes (preferred)
• AWS / Azure / GCP