MLOps Engineer
Insight Global
Job Description
This role is responsible for building, deploying, and maintaining machine learning pipelines and infrastructure in a production AWS environment. The engineer will support the operationalization of AI/ML across the organization, working closely with data science and engineering teams to ensure models are scalable, reliable, and cost-efficient. This position will focus on developing end-to-end ML workflows, implementing CI/CD pipelines, and establishing monitoring, observability, and FinOps practices. This role operates within a newly forming team and will contribute to building standards and best practices for ML platform operations.
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 View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:
Skills and Requirements
• 3–5+ years of experience in MLOps, DevOps, or cloud engineering
• Hands-on experience building and deploying ML pipelines in AWS (SageMaker, Lambda, S3)
• Production experience (1+ year) owning and supporting ML systems (monitoring, failures, retraining)
• Strong programming skills in Python for automation and pipeline development
• Experience with CI/CD pipelines and Infrastructure-as-Code (Terraform, CloudFormation, etc.)
• Experience with monitoring, observability, and alerting for production systems
• Exposure to ML lifecycle + cost optimization (FinOps) in cloud environments • Experience with Amazon Bedrock
• Experience with containerization (Docker, Kubernetes)
• Experience in regulated environments (insurance, healthcare, finance)
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