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Remote-friendly, mid-level seniority, metro market, and an attractive AI-native engineer title increase applicant competition.
Core LLM, Python, and cloud skills transfer across industries, but healthcare/regulatory context raises domain specificity.
Explicit 6+ years plus hard requirements for Kubernetes, cloud, and production LLM experience make shortlisting strict.
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Architect and build the full stack of the Genesis AI-native healthcare platform including agentic pipelines, backend APIs, data infrastructure, and clinical UI.
Own end-to-end development and foundational design decisions for intelligent systems operating at scale in a regulated industry.
Work directly with founders and customers to deliver production AI systems where LLMs perform real-world tasks, not just demos or proofs-of-concept.
6+ years building production web applications with strong Python skills (FastAPI, Django, or Flask).
Experience with cloud-native infrastructure: Docker, Kubernetes (mandatory), and at least one public cloud (AWS, Azure, or GCP) with operational experience.
Proven design and operation of AI-native systems using LLMs in production including prompt engineering, RAG pipelines, and AI observability.
Work Experience Required: Minimum 6 years; Kubernetes and cloud experience mandatory; Frontend skills (React, TypeScript) for clinical UI when needed.
Deep experience in production AI systems where LLMs are integrated as core infrastructure, not bolt-on features.
Strong backend engineering mindset with asynchronous, event-driven architecture experience and robust API design for human and AI agent consumption.
Comfortable with full-stack ownership including occasional frontend development; has operational expertise managing on-call production services at cloud scale.