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Job Description

Data Engineering Professional II

Digital, Data & Technology (DD&T;) - R&D; MLOPs

About The Role

We are seeking an MLOps Engineer to operationalize machine learning and generative AI across our R&D; and enterprise data ecosystem. You will build and maintain the platforms, pipelines, and controls that move models from notebook experiments into validated, production-grade, GxP-compliant services: supporting use cases that span clinical development, regulatory operations, pharmacovigilance, real-world evidence, and translational/biomarker research.

This is a hands-on engineering role at the intersection of data engineering, ML lifecycle automation, and regulated-systems discipline.





You will work primarily in Databricks and AWS, partnering with data scientists, platform/cloud engineering, quality, and regulatory teams to ship models that are reproducible, monitored, auditable, and trustworthy.

Key Responsibilities

ML Lifecycle & Pipeline Automation

Design, build, and operate end-to-end ML pipelines (data ingestion → feature engineering → training → validation → deployment → monitoring) using Databricks (Delta Lake, MLflow, Unity Catalog, Feature Store, Workflows/Jobs) and AWS services.
Implement CI/CD for ML and data assets (e.g., GitHub Actions, GitLab CI, or Jenkins), including automated testing, setting promotion (dev → test → prod), and reproducible builds.
Stand up and maintain model registries, model versioning, and artifact lineage so every deployed model is traceable to its data, code, and configuration.

Cloud & Platform Engineering (AWS)

Build and manage ML infrastructure on AWS — e.g., Sage

📌 Data Engineering Professional II (Bengaluru)
🏢 Bioksha
📍 Bengaluru