ML Ops Engineer

Insight Global; Virginia Beach (VA); On-site; USD 95,000 - 150,000; Full time

Benefits offered by this job

Healthcare insurance

Paid leave

Job summary

Insight Global is seeking an experienced MLOps Engineer to operationalize, scale, and support AI solutions in production across clinical, insurance, and financial domains. You will deploy models into production, integrate with Epic and other healthcare platforms, and collaborate with data scientists and platform teams.

The role emphasizes automation, monitoring, and governance, with current Azure Machine Learning and a move toward Databricks.

Qualifications

• 5+ years in MLOps, ML engineering, Data Engineering, or related field.
• Experience deploying ML models into production environments.
• Hands-on experience building and maintaining CI/CD pipelines.

Responsibilities

• Deploying and managing machine learning models in production environments.
• Building and maintaining CI/CD pipelines for ML workflows.
• Developing monitoring, alerting, and automation processes for model performance and reliability.
• Integrating AI models and datasets into existing enterprise platforms, including Epic and other healthcare systems.
• Supporting the migration and optimization of AI workloads within Azure ML and Databricks environments.
• Collaborating with data science and engineering teams to operationalize new AI solutions.
• Ensuring model scalability, reliability, governance, and performance across the organization.

Skills

MLOps

Machine Learning

Data Engineering

CI/CD

Azure ML

Databricks

Model governance

Production monitoring

Healthcare data

Tools

Epic

Azure

Databricks

CI/CD tools

Job description

5+ years of experience in MLOps, Machine Learning Engineering, Data Engineering, or a related field

Experience deploying machine learning models into production environments

Hands-on experience building and maintaining CI/CD pipelines

Experience with machine learning infrastructure, deployment automation, and production support

Experience monitoring, troubleshooting, and optimizing machine learning models in production

Experience working with Azure Machine Learning and/or similar cloud-based AI platforms

Experience integrating data sources into machine learning solutions

Understanding of machine learning lifecycle management and model governance

Nice to Have Skills & Experience

• Healthcare industry experience
• Experience working with Epic data or healthcare application integrations
• Experience supporting AI solutions within clinical, insurance, or financial business functions
• Experience building enterprise-scale AI platforms
• Familiarity with model observability, MLOps frameworks, and AI governance best practices

Job Description

Our client is expanding its AI capabilities across multiple business areas, including clinical, insurance, and financial operations. They have several machine learning models already deployed within their environment and are seeking an experienced MLOps Engineer to help operationalize, scale, and support AI solutions in production.

This individual will be responsible for deploying machine learning models into production environments, integrating them within existing healthcare technology platforms such as Epic, and ensuring reliable model performance through automation and monitoring. The MLOps Engineer will work closely with data scientists, AI engineers, and platform teams to create the infrastructure and deployment pipelines necessary to support enterprise AI initiatives. The environment is currently leveraging Azure Machine Learning with a strategic transition toward Databricks.

Key responsibilities include:

• Deploying and managing machine learning models in production environments
• Building and maintaining CI/CD pipelines for machine learning workflows
• Developing monitoring, alerting, and automation processes for model performance and reliability
• Integrating AI models and datasets into existing enterprise platforms, including Epic and other healthcare systems
• Supporting the migration and optimization of AI workloads within Azure ML and Databricks environments
• Collaborating with data science and engineering teams to operationalize new AI solutions
• Ensuring model scalability, reliability, governance, and performance across the organization.

Compensation:

$95,000.00 to $150,000.00 per year annual salary.

Exact compensation may vary based on several factors, including skills, experience, and education.

Benefit packages for this role may include healthcare insurance offerings and paid leave as provided by applicable law.