Senior Python Backend Engineer — AI Health APIs (Hybrid NYC)

CBTS; New York (NY); Hybrid; USD 147.000 - 170.000; Vollzeit

Zusammenfassung

CBTS in New York City is seeking a Senior/Staff Python Backend Developer to design scalable APIs and services powering AI-driven medical data applications. This onsite hybrid role requires 3 days in the office and offers a salary target of 147K–170K plus bonus.

You will collaborate across data science, underwriting, product and customer success teams, shaping API architecture, optimizing performance, and expanding with ML models and large language models as the product evolves.

Qualifikationen

• Backend software development experience with Python.
• Strong knowledge of API design and OpenAPI.
• Experience with cloud platforms and container orchestration (Kubernetes).
• Familiarity with SQL databases and ORM (SQLAlchemy).
• Ability to collaborate across engineering, data science, underwriting, and product teams.
• Willingness to work onsite in NYC with hybrid schedule.

Aufgaben

• Shape API architecture for real-time decisioning on medical data.
• Collaborate with data scientists, underwriters, customer success and product managers to deliver the roadmap.
• Optimize backend performance and scalability for large volumes of health data.
• Adapt the product as ML models and large language models evolve.
• Write unit/integration tests and participate in code reviews.

Kenntnisse

Python

API design

Cloud computing

Collaboration

Ausbildung

Bachelors degree in Engineering/CS/Data Science

Tools

Docker

Kubernetes

OpenAPI/Swagger/FastAPI

SQLAlchemy

PostgreSQL/MySQL

Terraform

Jobbeschreibung

CBTS in New York City is seeking a Senior/Staff Python Backend Developer to design scalable APIs and services powering AI-driven medical data applications. This onsite hybrid role requires 3 days in the office and offers a salary target of 147K–170K plus bonus.

You will collaborate across data science, underwriting, product and customer success teams, shaping API architecture, optimizing performance, and expanding with ML models and large language models as the product evolves.