REMOTE AI/ML Engineer II

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

Job Description

Insight Global is seeking a Machine Learning Engineer II for a leading healthcare organization focused on advancing data-driven innovation. This candidate will help build and scale a growing machine learning ecosystem, supporting the deployment, automation, governance, and monitoring of ML models in production. The ideal candidate comes from a strong technical background in software engineering, data engineering, analytics, or machine learning and has successfully transitioned into AI/ML-focused work. This person will collaborate closely with cross-functional teams to develop scalable ML infrastructure, improve operational efficiencies, automate model lifecycle processes, and support cloud-based solutions. This is an exciting opportunity to join a greenfield environment where you can help shape machine learning operations from the ground up while working with modern technologies such as Azure Fabric, GitHub, and enterprise-scale data platforms.

Day-to-Day

• Develop and refine machine learning deployment pipelines and workflows for production environments
• Support lifecycle governance, versioning, and reproducibility of ML models
• Implement and maintain model monitoring, performance tracking, and drift detection systems
• Build dashboards and alerting mechanisms for model performance visibility
• Partner with data scientists, software engineers, and DevOps teams on ML solutions
• Automate model retraining, validation, and deployment processes
• Apply healthcare data governance and security standards to ML systems
• Optimize and scale cloud infrastructure supporting machine learning workloads
• Create technical documentation and contribute to best practices and knowledge sharing
• Support a greenfield ML environment and help establish foundational ML operational processes

Skills and Requirements

Must-Haves

• 4-5 years of hands-on AI/ML experience
• Background in software development, data engineering, data analytics, or similar technical discipline
• Strong coding/programming experience
• Experience supporting machine learning models in production environments
• Knowledge of MLOps practices, ML lifecycle management, deployment, monitoring, and automation
• Experience with cloud platforms and ML infrastructure optimization
• Familiarity with CI/CD pipelines, containerization, and cloud infrastructure
• Experience working in enterprise-level data environments
• Exposure to GitHub and Azure Fabric environments
• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)

Plusses

• Healthcare industry experience
• Experience in regulated environments
• Cloud certifications (Azure, AWS, GCP)
• Machine Learning or DevOps certifications
• Experience with VS Code
• Prior MLOps, DevOps, or ML Engineering experience

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to [e-mail].