Machine Learning Engineer

About This Role

This engineering role focuses on designing, productionizing, deploying, and operating machine learning solutions to support management decisions within the Data Platforms & Solutions team. It bridges data science, data engineering, and software engineering, collaborating with various teams to translate experimental models into scalable and reliable production systems on Databricks.

Responsibilities

• Design, build, deploy, and maintain production-grade machine learning solutions supporting demand forecasting, dynamic pricing, inventory optimization, and recommendation use cases
• Partner with data scientists to convert experimental notebooks and prototypes into modular, tested, repeatable training and inference pipelines
• Build and maintain feature engineering, model-training, batch-scoring, and real-time inference workflows using Python, Spark, SQL, Databricks, and MLflow
• Implement MLOps practices for experiment tracking, model registration and versioning, automated testing, CI/CD, controlled promotion, rollback, and reproducibility
• Define and monitor model and operational health measures, including model quality, drift signals, latency, failures, data quality, resource utilization, and compute cost
• Validate model changes against reproducible datasets and established baselines, and assess downstream impact
• Integrate model outputs with business applications and downstream platforms through reliable batch jobs, APIs, and secure data-access patterns
• Optimize model architecture, feature processing, training, and inference for scalability, reliability, maintainability, and cost efficiency
• Implement safeguards and fallback behavior so invalid data or failed optimization does not result in unsafe or misleading recommendations
• Collaborate with stakeholders to translate revenue-management requirements into technical specifications, acceptance criteria, and measurable outcomes
• Create and maintain technical documentation, model cards, runbooks, deployment plans, monitoring procedures, and retraining and rollback strategies
• Ensure solutions comply with enterprise data governance, privacy, security, audit, and responsible AI requirements

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Data Science, Statistics, Applied Mathematics, or a related field; or equivalent practical experience
• 4+ years of relevant experience in machine learning engineering, applied data science, data engineering, or software engineering for production ML systems
• Strong proficiency in Python and SQL, including experience building maintainable, testable production code
• Hands-on experience developing, deploying, and supporting machine learning models in production environments
• Experience with cloud platforms, with a preference for Microsoft Azure