Job Description
ML Platform & Pipelines:
• Design and implement end-to-end ML pipelines for training, validation, packaging, and deployment.
• Create reusable templates and tooling for experimentation, feature consumption, and model lifecycle management.
CI/CD & Release Engineering:
• Build CI/CD for ML (tests, quality gates, approvals) using Azure DevOps/GitHub Actions.
• Manage model registry, artifact versioning, and environment reproducibility.
Deployment, Monitoring & Reliability:
• Implement batch and real-time serving patterns; automate monitoring for drift, performance, and data quality.
• Establish SLOs/SLAs for ML services; lead incident response and root-cause analysis for ML production issues.
Cloud & Security:
• Operate ML infrastructure on Azure (preferred), including compute, networking, IAM, and secrets management.
• Apply governance: access controls, auditability, and compliance requirements.
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 HR@insightglobal.com.To 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/.
Required Skills & Experience
• 5–8+ years of experience in ML Ops, DevOps, Data Engineering. Data Science, or related roles.
• Hands-on experience with Azure ML and/or Databricks, MLflow, and asset bundles.
• Kubernetes and orchestration experience; familiarity with model serving frameworks.
• Strong Python and SQL skills; experience with automated testing and observability.
• Experience with monitoring/alerting (e.g., Azure Monitor, Prometheus/Grafana).
Desired Skills:
• Infrastructure-as-Code (Terraform/Bicep) and policy-as-code experience.
• Experience with feature stores and data quality frameworks.
• Experience supporting regulated data environments and security reviews.
Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.