AWS Sagemaker Engineer

Insight Global is seeking a hands‑on AWS SageMaker Engineer to support the build‑out of a new, dedicated SageMaker environment for Wealth Management. This role is purely development and engineering focused, responsible for building, deploying, and operationalizing machine learning models and MLOps pipelines — not high‑level architecture or strategy. The ideal candidate has strong experience developing production‑ready ML solutions in AWS SageMaker, working closely with data scientists and platform teams to onboard and deploy models in a secure, regulated financial services environment.

Responsibilities
- Build and support a new AWS SageMaker instance for Wealth Management use cases
- Develop and deploy end‑to‑end ML pipelines using AWS SageMaker (training, tuning, deployment, monitoring)
- Implement hands‑on MLOps engineering, including:
○ CI/CD for ML workflows
○ Model versioning and promotion
○ Monitoring and operational support
- Onboard and deploy new ML models into SageMaker production environments
- Develop reusable engineering components and templates for SageMaker workflows
- Integrate SageMaker with AWS services such as S3, Lambda, Step Functions, CloudWatch, and IAM
- Partner closely with data scientists to productionize models
- Troubleshoot model deployment, performance, and pipeline issues
- Contribute to documentation, runbooks, and engineering standards

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/.

- 5+ years of hands‑on experience in ML engineering, data engineering, or cloud engineering roles in financial services enterprise environments, ideally Wealth Management
- Strong, hands‑on experience with AWS SageMaker, including:
○ Training jobs
○ Pipelines
○ Model deployment and endpoints
- Strong Python development skills
- Experience implementing MLOps practices in production environments
- Experience building CI/CD pipelines for ML using AWS-native tools
- Solid understanding of ML lifecycle management in production
- Experience working in regulated environments (financial services preferred)
- Ability to work independently and deliver engineering solutions end‑to‑end

- Wealth Management, Banking, or Capital Markets experience
- Experience onboarding multiple ML models into SageMaker
- Familiarity with infrastructure‑as‑code (Terraform or CloudFormation)
- Experience with model monitoring, governance, or risk controls
- AWS Certification (Machine Learning or Solutions Architect)

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.