- 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)
- 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)
🏢 Tredence
📍 Hyderabad
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