Lead AiOps Engineer

Tredence Inc.; Hyderabad, Pune District, Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Tredence Inc. is seeking a senior MLOps Architect to lead the design and execution of enterprise-grade ML platforms on Azure Databricks. You will own the full ML lifecycle, drive governance, and mentor teams to deploy production-grade models.

The role requires deep expertise in Databricks MLOps, CI/CD for ML, and governance frameworks, with a focus on scalable, secure, and reliable ML solutions across the enterprise.

Qualifications

• Experience with enterprise-scale MLOps on cloud platforms.
• Proficiency in end-to-end ML lifecycle management.
• Strong understanding of feature stores and model registries.
• Familiarity with governance, security, and compliance in ML systems.

Responsibilities

• Lead design and implementation of enterprise-scale MLOps on Azure Databricks.
• Own end-to-end ML lifecycle: feature engineering, training, deployment, monitoring, governance, support.
• Define scalable ML platform architecture leveraging Databricks-native capabilities.
• Establish CI/CD, model versioning, governance, monitoring standards.
• Mentor data scientists and ML engineers on MLOps patterns.
• Collaborate with business, data eng, and AI teams on prod ML solutions.

Skills

Databricks MLOps

Azure Databricks

PySpark

Python

ML lifecycle management

CI/CD for ML

Model monitoring

Data governance

Terraform

REST APIs

Git

Azure DevOps

MLOps architecture

Tools

MLflow

Unity Catalog

Databricks Asset Bundles

Azure DevOps

GitHub Actions

Terraform

Job description

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