Job Title

Location : Bangalore Work : Onsite (Mon - Thu) and Remote on FriJob Title Machine Learning Engineer (Data Engineering Focus) – Databricks | Retail Grocery Overview We are seeking aMachine Learning Engineer with strong Data Engineering skillsto build and operationalize scalable data and ML solutions onDatabricks running on Google Cloud Platform (GCP) . This role focuses on developingend-to-end data pipelines, feature engineering, and production ML workflowsthat power critical retail grocery use cases such asdemand forecasting, personalization, promotions, pricing, and inventory optimization . You will work across data engineering, data science, and platform teams to deliverreliable, production-grade ML systemsat scale. Key Responsibilities Data Engineering & Platform Design, build, and optimizebatch and streaming data pipelinesusingDatabricks (Spark / Structured Streaming) . Develop robustETL/ELT pipelinesingesting retail data (POS, transactions, customer, inventory, promotions, supplier data). ImplementDelta Laketables with best practices for performance, schema evolution, and data quality. Orchestrate pipelines usingDatabricks Workflowsand/orCloud Composer (Airflow) . Ensuredata reliability, observability, and cost efficiencyacross pipelines. Machine Learning Engineering Build and productionize ML pipelines usingDatabricks MLflow ,Databricks Feature Store , andSpark ML / Python ML frameworks . Collaborate with data scientists to convert experiments intoscalable, reusable ML pipelines . Deploy and managebatch and real-time inferenceworkflows within Databricks. Optimize model training and inference for performance and cost. MLOps & Best Practices ImplementML lifecycle managementusingMLflow(experiment tracking, model registry, versioning). EnableCI/CD for data and ML pipelinesusing Git-based workflows. Monitor model performance, data drift, and pipeline health. Enforce best practices aroundtesting, code quality, and reproducibility . Retail Analytics & Collaboration Partner with business, analytics, and product teams to translateretail grocery use casesinto data and ML solutions. Provide technical guidance onSpark optimization, data modeling, and ML architecture . Contribute to platform standards and reusable components. Required Qualifications 4+ yearsof experience inData Engineering and/or Machine Learning Engineering . Strong hands-on experience withDatabricks : Apache Spark (PySpark / Spark SQL) Delta Lake MLflow Strong proficiency inPythonandSQL . Experience buildingproduction-grade data pipelinesat scale. Solid understanding ofML concepts , feature engineering, and model evaluation. Experience deploying ML models in distributed environments. Preferred Experience withDatabricks on GCP . Familiarity withCloud Composer (Airflow) . Experience withBigQuery ,Pub/Sub , or GCP storage services. Retail, grocery, e-commerce, or CPG domain experience. Experience withdemand forecasting, recommendation systems, or pricing models . Exposure toreal-time/streaming MLuse cases.