Lead/Staff Full Stack Engineer, AI Platform & Agents
Role details
Job location
Tech stack
Job description
In Health, we're launching UpToDate Expert AI-a medical research and clinical reasoning agent that transforms the world's most widely used point-of-care knowledge resource into a real-time medical assistant. Millions of physicians will rely on it to accelerate differential diagnosis, refine treatment decisions, and reduce cognitive load-while maintaining rigorous safety, privacy, and guideline fidelity. Improvements you ship (latency, reliability, hallucination reduction) will translate directly into faster, higher-quality patient care at global scale.
What you'll do
- Design and implement full-stack applications, AI agents, and platform components that enable rapid GenAI agent development, validation, and deployment.
- Build developer tooling, CI/CD, and observability for safe, fast iteration (evals, canaries, rollout/rollback, cost and quality telemetry).
- Apply secure SDLC and privacy-by-design practices (threat modeling, least privilege).
- Collaborate with product, UX, and domain experts to deliver customer-focused solutions with measurable outcomes.
- Apply current LLM patterns (RAG, retrieval, routing, tool-use, evals) to deliver measurable customer value-faster, more reliable AI systems; reduced time-to-decision; improved trust/safety metrics; and lower cost per query.
- Lead by example and be heavily hands-on: drive architecture, mentor engineers, and take ownership of larger projects.
Requirements
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5+ years of professional software engineering experience.
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Strong full-stack development skills and cloud experience (AWS/Azure/GCP).
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Expert in at least one, and proficient across the others: AI Agent development and evaluation Backend development Frontend development Cloud services (AWS/Azure/GCP) CI/CD and Infrastructure as Code Site Reliability Engineering (SRE) Quality engineering / testing strategy Secure SDLC and privacy by design
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Proven track record delivering secure, reliable, cloud-native systems to production. Nice to have
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Proven ability to deliver software products independently or as part of a small, fast-paced team.
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Experience of taking AI agents from concept to production, including safety evaluations, iterative testing (e.g., A/B testing), and continuous improvement.
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Experience with LangChain/LangGraph and MCP; vector/RAG systems; OpenSearch.
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Worked on traditional ML tasks like training, deployment, and monitoring.
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Understand how LLMs work, their failure modes, and techniques like fine-tuning and model adaptation.
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Familiarity with regulatory frameworks such as SOC2, HIPAA, etc.
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Excellent problem-solving, ownership, and cross-functional communication.