Citizen Health
Healthcare
SeniorAIEngineer
“Senior AI Engineer at Citizen Health. Skills: agentic LLM systems, RAG pipelines, tool-use frameworks, evaluation infrastructure, production LLM applications. Design and implement agentic LLM systems. Build RAG pipelines”
What You'll Achieve.
build an autonomous agent that operates reliably; ship production systems that real patients depend on daily; ensure the advocate's responses are grounded in the patient's actual medical history; make the advocate's WhatsApp conversations feel natural, contextual, and proactive; make AI systems reliable at the 95th percentile; optimize inference costs
Industry & Context.
problem-solving; analytical skills
What They're Looking For.
Must Have
4+ years of software engineering experience, 2+ years building LLM-powered applications in production, proficiency in Python, hands-on experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent), Experience designing and implementing RAG systems — including chunking strategies, embedding models, vector databases, retrieval evaluation, and hybrid search, Track record building AI systems that take real-world actions (tool-use, function calling, browser automation, API orchestration), not just generate text, software engineering fundamentals — you write production-grade code with tests, logging, error handling, and clear abstractions, Experience with evaluation and testing of LLM systems — you've built evals, measured hallucination rates, and designed regression suites for non-deterministic outputs, Comfort with ambiguity and fast iteration, written and verbal communication
Nice to Have
Experience with healthcare, life sciences, or other regulated domains where AI output accuracy has high stakes (HIPAA compliance, clinical data handling), Familiarity with multi-agent architectures — designing systems where multiple specialized agents collaborate, hand off tasks, and share context, Experience with WhatsApp Business API, messaging platforms, or conversational AI at scale, Background in inference cost optimization — model selection, caching, batching, routing between models of different capability/cost profiles, Familiarity with medical data formats (FHIR, HL7, CDA) or experience building systems over medical records, Experience with voice AI, telephony APIs, or browser automation frameworks (Playwright, Puppeteer), Contributions to open-source AI/LLM tooling or active engagement with the agentic AI community
What You'll Do.
Design and implement agentic LLM systems
Develop tool-use frameworks
Create evaluation infrastructure
Design and implement multi-agent architecture
Build retrieval pipelines over patient medical records
condition-specific knowledge bases
and community-sourced data
Build and maintain frameworks for real-world actions (browser automation
Design systems for natural
and proactive WhatsApp conversations
Build evaluation frameworks for agentic outputs
Develop testing infrastructure for multi-step agent workflows
Design prompt architectures
Optimize inference costs
How You'll Work.
Team & Collaboration
Work closely with Product; Work closely with Platform Engineering; Work closely with clinical advisors; Explain agentic architecture tradeoffs to a PM; Debug a prompt chain with an engineer
Communication Scope
written communication; verbal communication; explain agentic architecture tradeoffs to a PM; debug a prompt chain with an engineer
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