MLOps Engineer

Insight Global; San Diego (CA); On-site; USD 120,000 - 165,000; Full time

Job summary

Insight Global is seeking an experienced MLOps Engineer to build, deploy, and support production ML infrastructure on AWS. You will partner with Data Scientists, Cloud Engineers, and application teams to productionize models, automate deployment pipelines, and establish monitoring, observability, governance, and FinOps best practices.

The role focuses on hands-on engineering to create scalable, reliable ML platforms, enabling AI solutions to move efficiently from development into production

Qualifications

• 3–5+ years of experience in AWS Cloud Engineering, DevOps, Platform Engineering, or MLOps.
• Hands-on experience building and deploying ML pipelines in AWS using SageMaker, Lambda, S3, and Step Functions.
• 1+ year supporting production ML systems, including monitoring, troubleshooting, retraining, and operational support.
• Strong Python development experience.
• Experience building and supporting CI/CD pipelines.
• Infrastructure‑as‑Code with Terraform, CloudFormation, or AWS CDK.
• Experience partnering with Data Scientists to productionize ML models.
• Strong ML lifecycle knowledge: deployment, monitoring, observability, retraining, and production support.
• Experience with Amazon Bedrock, Docker, Kubernetes/ECS/EKS, and ML observability tools.
• Experience with feature stores and model governance in regulated environments (insurance/healthcare/financial services).

Responsibilities

• Build and deploy production ML infrastructure on AWS.
• Collaborate with Data Scientists, Cloud Engineers, and applications teams to productionize models.
• Automate deployment pipelines and establish monitoring, observability, governance, and FinOps standards.
• Develop scalable, reliable ML platforms to move AI solutions from development to production.

Skills

Python development

CI/CD pipelines

MLOps

Cloud engineering

ML lifecycle knowledge

Collaboration with Data Scientists

Tools

AWS SageMaker

Terraform

CloudFormation

AWS CDK

Docker

Kubernetes

ECS

EKS

Bedrock

ML observability tools

Feature stores

Model governance frameworks

S3

Lambda

Step Functions

Job description

Job Description

A large insurance customer is building a new AI/ML Platform team responsible for operationalizing machine learning across the enterprise. They are seeking an MLOps Engineer to help build, deploy, and support production machine learning infrastructure within AWS. This engineer will partner closely with Data Scientists, Cloud Engineers, and Application teams to productionize ML models, automate deployment pipelines, and establish monitoring, observability, governance, and FinOps best practices. This is a hands‑on engineering role focused on building scalable, reliable ML platforms that enable AI solutions to move efficiently from development into production while helping establish standards for a newly formed enterprise AI organization.

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 opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their 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 recruiting process, please send a request to [e-mail] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

• 3–5+ years of experience in AWS Cloud Engineering, DevOps, Platform Engineering, or MLOps.
• Hands‑on experience building and deploying ML pipelines in AWS using services such as SageMaker, Lambda, S3, and Step Functions.
• 1+ year supporting production ML systems, including monitoring, troubleshooting, retraining, and operational support.
• Strong Python development experience.
• Experience building and supporting CI/CD pipelines.
• Infrastructure‑as‑Code experience with Terraform, CloudFormation, or AWS CDK.
• Experience partnering directly with Data Scientists to productionize machine learning models.
• Strong understanding of the ML lifecycle, including deployment, monitoring, observability, retraining, and production support.
• Experience with Amazon Bedrock.
• Experience with Docker and containerization technologies.
• Experience with Kubernetes, ECS, and/or EKS.
• Experience with ML observability tools.
• Experience with feature stores and model governance frameworks.
• Experience working in regulated environments such as insurance, healthcare, or financial services.