Machine Learning Engineer

Location : Bangalore

Work : Onsite (Mon - Thu) and Remote on Fri

Job Title

Machine Learning Engineer (Data Engineering Focus) Databricks | Retail Grocery

Overview

We are seeking a Machine Learning Engineer with strong Data Engineering skills to build and operationalize scalable data and ML solutions on Databricks running on Google Cloud Platform (GCP) . This role focuses on developing end-to-end data pipelines, feature engineering, and production ML workflows that power critical retail grocery use cases such as demand forecasting, personalization, promotions, pricing, and inventory optimization .

You will work across data engineering, data science, and platform teams to deliver reliable, production-grade ML systems at scale.

Key Responsibilities

Data Engineering & Platform

• Design, build, and optimize batch and streaming data pipelines using Databricks (Spark / Structured Streaming) .
• Develop robust ETL/ELT pipelines ingesting retail data (POS, transactions, customer, inventory, promotions, supplier data).
• Implement Delta Lake tables with best practices for performance, schema evolution, and data quality.
• Orchestrate pipelines using Databricks Workflows and/or Cloud Composer (Airflow) .
• Ensure data reliability, observability, and cost efficiency across pipelines.

Machine Learning Engineering

• Build and productionize ML pipelines using Databricks MLflow , Databricks Feature Store , and Spark ML / Python ML frameworks .
• Collaborate with data scientists to convert experiments into scalable, reusable ML pipelines .
• Deploy and manage batch and real-time inference workflows within Databricks.
• Optimize model training and inference for performance and cost.

MLOps & Best Practices

• Implement ML lifecycle management using MLflow (experiment tracking, model registry, versioning).
• Enable CI/CD for data and ML pipelines using Git-based workflows.
• Monitor model performance, data drift, and pipeline health.
• Enforce best practices around testing, code quality, and reproducibility .

Retail Analytics & Collaboration

• Partner with business, analytics, and product teams to translate retail grocery use cases into data and ML solutions.
• Provide technical guidance on Spark optimization, data modeling, and ML architecture .
• Contribute to platform standards and reusable components.

Required Qualifications

• 4+ years of experience in Data Engineering and/or Machine Learning Engineering .
• Strong hands-on experience with Databricks :
• Apache Spark (PySpark / Spark SQL)
• Delta Lake
• MLflow
• Strong proficiency in Python and SQL .
• Experience building production-grade data pipelines at scale.
• Solid understanding of ML concepts , feature engineering, and model evaluation.
• Experience deploying ML models in distributed environments.

Preferred

• Experience with Databricks on GCP .
• Familiarity with Cloud Composer (Airflow) .
• Experience with BigQuery , Pub/Sub , or GCP storage services.
• Retail, grocery, e-commerce, or CPG domain experience.
• Experience with demand forecasting, recommendation systems, or pricing models .
• Exposure to real-time/streaming ML use cases.