Senior Machine Learning Engineer

Bollinger Shipyards, Inc.; Metairie (LA); On-site; USD 140,000 - 210,000; Full time

Job summary

Bollinger Shipyards, Inc. is seeking a Senior Machine Learning Engineer to operationalize AI/ML solutions into scalable, production-ready enterprise systems across manufacturing and operations.

You will bridge data science, software engineering, and infrastructure to deploy, monitor, and optimize AI offerings. The role emphasizes collaboration with Data Scientists, Data Engineers, and IT to ensure secure, reliable model serving, governance, and lifecycle management in on-site environments.

Qualifications

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

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 procedure

Skills

Azure ML

Databricks

ML Ops

Cloud AI platforms

Python

Docker

Kubernetes

IaC

Monitoring

Governance

Education

Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or related field

Tools

Docker

Kubernetes

IaC

Job description

POSITION OVERVIEW

We are seeking a dedicated Senior Machine Learning Engineer with at least a Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or related field. The candidate will operationalize 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.

REQUIREMENTS

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Management, or related field
• Minimum of 6–10 years of experience 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 practice

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 procedure