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Harnham Linkedin · Posted 23d ago

Principal AI Engineer

Oakland, California, United States

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Indexed description

Principal/Founding AI Engineer specializing in Agentic GenAI & Medical Data

Company Overview

Join a rapidly growing medical-tech startup, backed by $75M+ in funding, building a 3-sided marketplace transforming how healthcare connects, transacts, and personalizes patient experiences. We’re a product-led, remote-friendly team with engineering and commercial operations in the US and Croatia.


Role Overview

As a Senior Software Engineer, you’ll architect and develop agentic GenAI workflows leveraging Python, Django, and cutting-edge AI/ML tools. You will be responsible for designing internal platforms built on sensitive medical datasets, deploying market-facing solutions, and collaborating closely with commercial and AI teams to iterate rapidly.


Key Responsibilities

  • Build agentic GenAI workflows using proprietary and medical datasets.
  • Lead GenAI & workflow orchestration for healthcare data and knowledge management.
  • Develop and optimize marketing technology: email delivery, user engagement, personalization solutions, and growth engineering initiatives.
  • Prototype, productize, and validate commercial AI models—understand and model clinician behavior, prescription workflows, and intelligent decision systems.
  • Write production code for backend (Python/Django), ensure robust system architecture, and lead technical implementation cycles.
  • Partner with cross-functional teams across the US and Croatia to deliver rapid, validated product iterations.


Skills & Experience

  • Proven backend expertise: Python and Django in production environments.
  • Frontend experience: React (modern best practices).
  • Cloud/infrastructure: AWS (deploying, scaling, monitoring).
  • Databases: PostgreSQL; ability to optimize for large medical/structured datasets.
  • AI/ML: Experience with OpenAI and agent knowledge systems strongly preferred. Track record training or managing AI agents a plus.
  • Nice-to-have: Direct experience handling medical/healthcare data.
  • Scalability: Experience architecting knowledge systems with large-volume data indexing, not high-throughput but focused on effective information retrieval.

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