RTH - Y Total Experience - 3 -4 Years Relevant Experience - 3+ years Mandatory Skills: MLOps with hands-on work in Azure and Databricks. Experience implementing ML Ops / ML Engineering functionality in Databricks and Azure. Experience in building and maintaining CI/CD pipelines to automate model development, testing validation, and deployment. Experience in containerizing models using Docker or similar tools for deployment in production. Working knowledge of MLflow for tracking model artifacts,logging key metrics, and registering models with MLflow Model Registry or Azure Machine Learning Registry. Experience using MLflow open-source technology on the Databricks platform for model management (Model Governance, Model Cataloguing, and Model Documentation, etc.) Proven experience in deployment, automation, and maintenance of ML models in production environments. Experience in automation of model scheduling/triggering and orchestration of model pipelines Proficient in Python. Good to have(Not Mandatory) : Data Engineering experience. Experience preparing production-grade data pipelines, including ETL workflows to feed data into the Feature. Store or Data Lake. Familiarity with ensuring Feature Store and required datasets are accessible and up to date for model inference in production. Experience in optimizing model execution and ML pipelines using techniques such as data chunking, parallel processing, and task-level concurrency to improve performance and scalability. Exposure to automating model checks and model diagnostics. Detailed Job Description: This role requires experience across MLOps, Data Engineering, and Azure ecosystem, with a strong focus on operationalizing model workflows using Databricks, Azure services and MLflow. The candidate will be expected to work on automating the ML lifecycle through CI/CD pipelines, manage model training and registration using MLflow, and ensure efficient and reliable deployment and maintenance of models in production. Will be collaborating with data scientists, engineers, and operations teams to manage the production ML lifecycle. The role also involves working closely with Data Scientists to support model packaging and deployment, automate orchestration of model pipelines, and contribute to overall production readiness of ML systems. Familiarity with data pipelines, model governance, and production monitoring will be an added advantage. Location - Any Note - Resource needs to ready for F2F Intv at IBM location based on account request and Day 1 reporting from DOJ. - U2XJKN