ML/AI Ops Engineer

About the Role 

We’re looking for an ML/AI Ops Engineer with 7+ years of experience to own the end-to-end operationalization of our ML/AI solutions, ensuring models move smoothly from development to scalable, reliable production products. In this role, you’ll design and automate CI/CD pipelines, build and optimize model lifecycle workflows, monitor deployed models for performance, drift, and reliability, and integrate intelligence products into various digital tools such as Copilot, Salesforce, and Tableau. You’ll collaborate closely with Data Scientists, Data Engineers, and Data Architects to transform high-quality research models into robust, production-grade products. This is a key role in shaping a modern ML/AI lifecycle—from CI/CD to high governance. 

What You’ll Do 

• Own the end-to-end operationalization of ML and AI solutions—from development to scalable, reliable production systemsthat integrateseamlessly with other digital tools. 
  • Design, automate, and maintain CI/CD pipelines for model training, testing, deployment, and retraining (Azure DevOps, Databricks). 
  • Build, optimize, and version model lifecycle workflows, ensuring reproducibility, lineage, and governance across the ML/AI platform. 
  • Monitor production models for performance, drift, reliability, and resource usage; implement automated retraining workflows. 
  • Optimize compute, storage, and orchestration across the Databricks platform to ensure efficient, cost-effective operations. 
  • Collaborate closely with ML/AI Scientists, Data Engineers, and DWH team to transform research-grade models into production-ready services. 
  • Contribute to advancing our ML/AI platform, tooling, automation standards, and best practices. 

What You’ll Bring 

• Solid experience in operationalizing ML/AI models, including deployment, automation, monitoring, and lifecycle management.
• Strong programming skills in Python,PySpark, and SQL with clean, efficient, production-ready code.
• Experienced in feature engineering with a practical understanding of data engineering fundamentals - designing, validating, and optimizing feature pipelines, and ensuring feature consistency
• Experience in building Vector embeddings & RAG systems.
• Familiarity in ML and LLM models development and libraries used.
• Experience with MLflow (or similar tools) for model tracking, registry management, and lifecycle operations.
• Familiarity with CI/CD pipelines (Azure DevOps preferred)
• Strong grasp of data versioning, model versioning, reproducibility, and data lineage within governed ML/AI environments.
• Experience designing, consuming, or integrating REST APIs to expose ML/AI models as services and support real-time or near-real-time inference.
• Experience monitoring production models, identifying drift or performance issues, and implementing corrective workflows.
• A collaborative, systems-thinking mindset, working closely with ML/AI Scientists, Data Engineers, and Data Warehouse team. 

Bonus Skills 

• Understanding ofdata quality frameworks and how they integrate into ML pipelines.
• Comfort with infrastructure-as-code for provisioning and managing ML/AI platform components. 
• Working knowledge of Unix environments and general DevOps