Sr Cloud Machine Learning Engineer

HealthFirst

1d•$119,600 - $194,480

About The Position

The Senior Machine Learning Engineer will help to design data science systems. Coordinate with enterprise analytics leadership, IT partners and business stakeholders to understand their needs, inform system design and help implement complex solutions. About Healthfirst, Enterprise Analytics For over 30 years, Healthfirst has been serving New Yorkers. We helped pioneer the value-based healthcare model—where hospitals and physicians are paid based on patient outcomes—because our company was founded on the belief that insurers need to be true partners in the health system. Enterprise Analytics is a fast paced, innovative team. Our mission is to drive personalized, relevant interactions that help our members live healthier lives. Insights from this team focus on improving health outcomes, increasing member satisfaction, making our business units more efficient and continuing to grow as a company. We’re looking for curious problem solvers to continue to push us forward. If you are member-focused, collaborative and passionate about data come join our growing community of 70+ analysts, engineers and data scientists. Join our team to help build and scale Healthfirst’s MLOps and GenAI enablement. The team designs, builds, and maintains machine learning and data pipelines, feature stores, and internal GenAI applications — as well as the tooling and automation that streamline the industrialization of data science work across the enterprise. This is a hands-on senior engineering role in a collaborative team that values excellence, pragmatism, and mentorship.

Requirements

BS in Computer Science or related discipline
Five (5) years of experience using scripting or programming languages
Three (3) years of experience managing the end-to-end ML model life cycle
One (1) year of experience deploying ML solutions with cloud computing services (e.g., AWS)
Experience working with semi-structured and unstructured data
Excited about being a part of the decision-making process

Nice To Haves

Masters degree
Familiarity with containerization methods
Experience with health-related data
Languages: Python, SQL, Typescript
Infrastructure & Automation: Terraform, GitHub Actions, Apache Airflow
Compute: AWS Lambda, EC2, ECS, EKS
Data & Storage: AWS S3, Redshift, Glue, Athena, DynamoDB, Apache Iceberg, DBT
ML/AI: AWS SageMaker, Bedrock
Observability: AWS CloudWatch, X-Ray

Responsibilities

Design, build, and maintain scalable ML and data pipelines, including feature stores and MLOps infrastructure.
Develop and deploy GenAI applications using AWS Bedrock and related frameworks.
Collaborate closely with data scientists, engineers, and business partners to create reproducible, observable ML systems.
Automate and improve our deployment and observability stack using Terraform, GitHub Actions, and AWS services (CloudWatch, X-Ray, Lambda).
Contribute to our transition to containerized, distributed architectures (Docker, OpenShift).
Mentor teammates help plan and prioritize work, and champion best practices in code quality, automation, and Agile delivery.
Additional duties as assigned.