Senior Machine Learning Engineer in Raceland

The Senior ML 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., * 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

· 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 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