Production ML Engineer — Pipelines, Scale & Impact

HeartCentrix Solutions; Town of Texas (WI); On-site; USD 120,000 - 170,000; Full time

Job summary

HeartCentrix Solutions seeks a hands-on Data Scientist/ML Engineer to join a centralized ML team supporting multiple business units. This role focuses on taking models and pipelines from development into production, with an emphasis on scalable, reliable systems.

You will write production-grade Python, refactor legacy code, optimize ML pipelines, and collaborate with data scientists and engineers to deploy and monitor ML solutions in cloud environments.

Qualifications

• MS or PhD in a quantitative field.
• Strong Python programming and debugging skills.
• Hands-on experience deploying ML models to production.
• Experience with scalable ML pipelines.

Responsibilities

• Build, improve, and productionize machine learning pipelines and models.
• Take ML solutions from prototype through deployment, monitoring, and optimization.
• Write clean, production-quality Python code with modularity and testing.
• Refactor and improve existing codebases alongside new models.
• Collaborate with data scientists and data engineers to scale solutions.
• Support data extraction, cleaning, analysis, and feature engineering.
• Design and optimize ML workflows, including orchestration and monitoring.
• Work with large datasets and cloud-based technologies to scale development.

Skills

Python

ML pipeline experience

Production-grade coding

Education

MS or PhD in Data Science, Computer Science, Statistics, ML, Applied Mathematics, or related quantitative field

Tools

Databricks

Spark

MLflow

CI/CD

Azure

Tableau

Power BI

Job description

HeartCentrix Solutions seeks a hands-on Data Scientist/ML Engineer to join a centralized ML team supporting multiple business units. This role focuses on taking models and pipelines from development into production, with an emphasis on scalable, reliable systems.

You will write production-grade Python, refactor legacy code, optimize ML pipelines, and collaborate with data scientists and engineers to deploy and monitor ML solutions in cloud environments.