Remote AI/ML Engineer II — Production ML & MLOps

Insight Global; Austin (TX); Remote; USD 110,000 - 180,000; Full time

Job summary

Insight Global is seeking a Machine Learning Engineer II to help build and scale a growing ML ecosystem in a healthcare-focused org. You’ll deploy, automate, govern, and monitor ML models in production while collaborating with data scientists, software engineers, and DevOps teams.

The role involves greenfield ML operations, modern technologies like Azure Fabric, GitHub, and enterprise data platforms, with emphasis on scalable ML infrastructure and governance.

Qualifications

• 4-5 years of hands-on AI/ML experience.
• Background in software development, data engineering, or ML.
• Strong coding/programming experience.
• Experience deploying ML models in production.
• Knowledge of MLOps, lifecycle management, and monitoring.
• Familiarity with CI/CD, containers, and cloud infra.
• Experience in enterprise data environments and GitHub/Azure Fabric.
• Bachelor’s degree in CS/Engineering/Data Science or equivalent.

Responsibilities

• Develop and refine ML deployment pipelines for production.
• Support governance, versioning, and reproducibility of models.
• Implement model monitoring, drift detection, and performance tracking.
• Build dashboards and alerts for model visibility.
• Collaborate with data scientists, software engineers, and DevOps.
• Automate model retraining, validation, and deployment.
• Apply healthcare data governance and security standards.
• Scale cloud infrastructure for ML workloads.

Skills

AI/ML experience

Software development

Data engineering

CI/CD pipelines

Cloud platforms

GitHub

Azure Fabric

MLOps

Model deployment

Monitoring systems

Security governance

Team collaboration

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Azure

AWS

GCP

VS Code

GitHub

Job description

Insight Global is seeking a Machine Learning Engineer II to help build and scale a growing ML ecosystem in a healthcare-focused org. You’ll deploy, automate, govern, and monitor ML models in production while collaborating with data scientists, software engineers, and DevOps teams.

The role involves greenfield ML operations, modern technologies like Azure Fabric, GitHub, and enterprise data platforms, with emphasis on scalable ML infrastructure and governance.