Machine Learning Engineer

• The Senior MLOps Engineer will play a critical role in operationalizing machine learning workflows that drive dynamic pricing and personalized consumer experiences.
• This position is foundational for sustaining and scaling the ML ecosystem, enabling faster deployments, reliable model governance, and automation across the Consumer ML & Personalization team. The role ensures continuity, protects experimentation velocity, and preserves operational maturity for all production ML systems and go-to-market testing.
• Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores to support model development and deployment.
• Establish automated, production-ready ML pipelines for multi-model inference, self-serve test orchestration, contextual bandits, and advanced reporting. Drift Detection & Model Observability
• Design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production.
• Standardize retraining, monitor inference drift, and automate performance checks to prevent stale models and undetected errors. Model Calibration & Versioning
• Develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle.
• Maintain CI/CD checks, versioning, and consistent environments between Dev and Prod. Low-Latency Orchestration
• Design and optimize reinforcement learning (RL) orchestration pipelines, including Contextual Bandits, for real-time execution in low-latency environments. Automated Training Pipelines
• Create automated frameworks for training, retraining, and validating ML models, enabling efficient experimentation and deployment. CI/CD for ML

Lead AI/ML Engineer

AI Engineer