Senior Machine Learning Engineer

About the Role

The Senior Machine Learning Engineer is responsible for operationalizing machine learning and AI solutions into scalable, reliable, and production-ready enterprise systems. This role bridges data science, software engineering, and infrastructure disciplines to deploy, monitor, optimize, and support AI solutions that drive operational and business outcomes.

Responsibilities

• Deploy, integrate, and maintain machine learning and AI solutions within enterprise workflows and operational systems
• Design and develop scalable ML pipelines, feature stores, APIs, and model-serving infrastructure
• Collaborate with Data Scientists to productionize models and improve deployment readiness
• Monitor model performance, drift, availability, and reliability across production environments
• Implement processes for model retraining, versioning, governance, and lifecycle management
• Partner with Data Engineering teams to support feature engineering and data pipeline integration
• Ensure ML solutions are secure, scalable, maintainable, and aligned with enterprise architecture standards
• Support AI applications across forecasting, operational optimization, bidding, scheduling, maintenance, and automation use cases
• Troubleshoot and resolve issues related to model deployment and operational performance
• Contribute to ML engineering standards, best practices, and platform improvements
• Document architecture, deployment processes, and operational support procedures

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, or related field
• 6–10 years in ML or software engineering
• Strong Python and ML deployment experience
• Experience with cloud ML systems

Skills

• Experience with Azure ML, Databricks, ML Ops, or similar cloud AI platforms
• Experience in manufacturing, industrial, operational, or engineering environments
• Familiarity with large language models, Generative AI, and intelligent automation
• Experience supporting enterprise AI applications integrated with ERP or operational systems
• Knowledge of monitoring, observability, and model governance practices
• Experience with Docker, Kubernetes, and infrastructure-as-code practices