MLOPS+ Databricks Engineer (RARR Job 5331)

We are seeking a ML Ops Engineer to play a critical role in operationalizing machine learning workflows that drive dynamic pricing and personalized consumer experiences This position focuses on building robust ML infrastructure and frameworks including drift detection model calibration versioning and reinforcement learning orchestration The ideal candidate will bring expertise in Databricks Unity Catalog and feature stores and a deep understanding of Git workflows Databricks workflows and automated ML training pipelines

Key Responsibilities

• ML Infrastructure Development Build and maintain scalable ML infrastructure on Databricks leveraging Unity Catalog and feature stores to support model development and deployment
• Drift Detection Frameworks Design and implement frameworks for detecting data and model drift ensuring continuous monitoring and high reliability of ML models in production
• Model Calibration  Versioning Develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle
• LowLatency Orchestration Design and optimize reinforcement learning RL orchestration pipelines including Contextual Bandits for realtime execution in lowlatency environments
• Automated Training Pipelines Create automated frameworks for training retraining and validating ML models enabling efficient experimentation and deployment
• CICD for ML Implement CICD best practices to streamline the deployment and monitoring of ML models integrating with Databricks workflows and Gitbased version control systems
• Collaboration Work closely with ML Scientists to ship deploy and maintain models
• Monitoring  Optimization Build tools for model performance monitoring operational analytics and drift mitigation ensuring reliable operation in production environments

Qualifications

• 5+ years in MLOps ML Engineering or related roles focusing on deploying and managing ML workflows in production environments Handson experience building drift detection systems model calibration frameworks and robust monitoring tools for ML pipelines
• Proficient in using Databricks Apace Spark ML Flow Unity Catalog and feature stores
• Expertise in deploying and orchestrating lowlatency ML models including reinforcement learning solutions like Contextual Bandits and Qlearning
• Experience designing automated training pipelines for ML models focussing on efficiency
• Strong knowledge of Git workflows CICD practices and tools like GitLab or similar
• Proficiency in Python SQL and big data processing tools like Spark
• Familiarity with ML lifecycle tools such as MLflow Kubeflow and Airflow

Strong understanding of model performance monitoring drift detection and retraining workflows