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# Senior Machine Learning Engineer in Raceland

- **Company:** Energy Jobline
- **Location:** Raceland, LA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Information Engineering, Digital Architecture, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, Management of Software Versions, Feature Engineering, Generative AI, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Operational Systems, Machine Learning Operations, Data Pipelines, Docker, Databricks
- **Published:** September 4, 2026
- **Apply:** https://www.energyjobline.com/job/senior-machine-learning-engineer-raceland-31471780

## About the Role

· 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

## Description

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


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