Lead AiOps Engineer

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