AWS Machine Learning Engineer

AWS Machine Learning Engineer – ML Engineer I

Born digital, UST transforms lives through the power of technology. We help clients create transformative experiences and human‑centered solutions.

UST is seeking an AWS Machine Learning Engineer to build, deploy, and optimize production‑grade machine learning solutions on AWS. The role involves working across the ML lifecycle from data preparation and feature engineering through model training, evaluation, deployment, and monitoring using AWS‑native services and modern MLOps practices.

• Design, develop, and product ionize ML solutions on AWS using services such as Amazon Sage Maker, Amazon S3, AWS Lambda, AWS Step Functions, and related analytics/integration services.
• Build and maintain reproducible training and inference workflows, including data preprocessing, model training, evaluation, and deployment automation.
• Implement real‑time, batch, serverless, or multi‑model inference patterns based on business and performance needs.
• Develop APIs or service integrations for model consumption by downstream applications.
• Collaborate with data scientists, platform engineers, Dev Ops teams, and application teams to operationalize models reliably and securely.
• Monitor model performance, data quality, drift, and operational health in production; support retraining and continuous improvement processes.
• Apply AWS security and governance best practices including IAM, encryption, logging, and auditable deployments.

• Technical
  
  Skills:
  
  
• 5+ years in software/ML engineering, with strong hands‑on experience in Python.
• 2+ years of production experience with Amazon Sage Maker for training and/or inference deployments.
• Strong grasp of supervised/unsupervised ML pipelines, model evaluation, feature engineering, and experiment reproducibility.
• Experience with Sage Maker Pipelines, model packaging/registration concepts, and deployment automation.
• Strong knowledge of AWS core services: S3, IAM, Lambda, Cloud Watch, API Gateway, ECR, and networking fundamentals.
• Experience enforcing least‑privilege access, data isolation, token‑based authentication (OAuth2/JWT).
• Proficient in the Model Context Protocol (MCP) open standard, with hands‑on experience setting up custom MCP Servers using official Type Script or Python SDKs.
• Hands‑on experience integrating custom MCP servers into Agentic AI frameworks.
• Solid understanding of security vectors unique to LLM orchestration, including tool validation, API sandboxing, input sanitization, and enterprise access control.
• Experience building RESTful or event‑driven services to expose ML capabilities.
• Experience with Amazon Bedrock or generative AI integration patterns on AWS.
• Experience with MLflow, experiment tracking, or model registry tooling.
• Experience with Feature Store, data quality checks, or model bias/fairness validation.
• Familiarity with Docker/containerized ML workloads.
• Understanding of model monitoring, operational metrics, logging, and troubleshooting in production.
• Knowledge of cloud security basics including least privilege, encryption, and secure secret/configuration handling.
• Nice‑to‑Have
  
  Skills:
  
  
• Polyglot engineering capabilities (multi-language fluency).
• Familiarity with Terraform or Cloud Formation for infrastructure as code.
• Exposure to streaming/event‑driven data pipelines using AWS‑native services.

Location:

Illinois (United States). Compensation Range: $72,000–$108,000 per year.

Full‑time employees accrue a minimum of 10 days paid vacation per year, 6 days paid sick leave, 10 paid holidays, and paid bereavement and jury duty leave. They are eligible for the company’s 401(k) Retirement Plan with employer matching and health, dental, and vision insurance. Additional voluntary benefits include short‑ and long‑term disability, Health Savings Account (HSA), and Flexible Spending Account (FSA).

Employees in states with more generous sick leave laws receive those benefits.

UST is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. UST considers qualified applicants with arrest or conviction records in accordance with state and local laws and “fair chance” ordinances.