Key Responsibilities
Lead the design and implementation of enterprise-scale MLOps solutions on Azure Databricks.
Own the end-to-end machine learning lifecycle, including feature engineering, model training, deployment, monitoring, governance, and production support.
Define and implement scalable ML platform architecture leveraging Databricks-native capabilities.
Establish best practices for CI/CD, model versioning, governance, monitoring, and operationalization of machine learning workloads.
Mentor and guide data scientists and machine learning engineers on MLOps standards and implementation patterns.
Work closely with business, data engineering, and AI teams to deliver production-grade ML solutions.

Mandatory Skills

Databricks MLOps
Databricks Feature Store (Offline and Online Feature Stores)
MLflow Model Registry and model lifecycle management
Unity Catalog for data, model, and feature governance
Model Monitoring and observability frameworks
Lakehouse Monitoring implementation and operations
Deployment using Databricks Asset Bundles (DAB)




CI/CD implementation for ML workloads

Cloud & Engineering
Microsoft Azure
Azure Databricks
PySpark
Python
REST APIs and deployment frameworks
Git, Azure DevOps /GitHub Actions

Architecture
MLOps Solution Architecture
Enterprise ML Platform Design
Model Deployment Strategies (Batch, Real-time, Streaming)
Security, Governance, and Compliance Frameworks

Preferred Skills
GenAI and LLM operationalization on Databricks
Model Serving and Endpoint Management
DataOps and DevOps integration
Terraform / Infrastructure as Code
Experience working with large-scale AI transformation programs

Leadership Expectations
Act as the technical lead for the Databricks ML platform.
Drive architecture decisions, design reviews, and implementation governance.
Lead customer discussions and provide technical guidance to stakeholders.

Own delivery quality, scalability, reliability

📌 Lead Aiops Engineer Hyderabad (India)
🏢 Tredence
📍 India