Lead Full-Stack Engineer - Backend Focus
Humana · Dallas, TX · Today
On-siteEngineering$129k–$178k/yrFull-time
Key Responsibilities
- Design and lead development of core backend services, REST and GraphQL APIs, and service-to-service integrations that connect all layers of Centerwell's AI product stack.
- Establish patterns for authentication, authorization, rate limiting, PHI access control, and audit logging. Ensure HIPAA compliance is embedded in platform design from day one.
- Define and implement reusable patterns for integrating AI capabilities into product APIs, including LLM routing, streaming responses, tool orchestration, and model output handling.
- Build logging, monitoring, and alerting infrastructure so the platform is operable and debuggable at scale.
- Contribute to product feature development end-to-end using TypeScript and Next.js, from server-side APIs to rendered UI.
- Drive architecture reviews and define backend engineering standards that the full engineering team builds on.
Required Qualifications
- B.S. or M.S. in Computer Science, Engineering, or a related field (or equivalent experience)
- 8+ years of software engineering experience with deep expertise in backend and platform engineering
- Strong proficiency in TypeScript and/or Python for API and service development
- Solid understanding of authentication (OAuth2, OIDC), API security patterns, and authorization model design
- Proven track record building scalable, production-grade REST or GraphQL APIs
- Working knowledge of HIPAA requirements, particularly secure transmission, storage, and access control for PHI
- Experience with Next.js and React sufficient for full-stack feature contribution
- Demonstrated ownership of complex backend systems end-to-end, from architecture through production operation
- Hands-on experience with AI-assisted development tools and LLM coding harnesses (Claude Code, Pi Agent, Cursor, Codex, or similar)
Preferred Qualifications
- Experience in healthcare, financial services, or other highly regulated industries
- Exposure to event-driven architecture, microservices, or API gateway patterns at scale
- Familiarity with AI/LLM integration patterns, including streaming APIs and tool-use orchestration
- Experience in a greenfield or team-founding engineering environment