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Specialized LLM skills reduce applicants, but metro location and mid-level seniority increase competition.
ML/LLM engineering skills transferable, but healthcare domain knowledge materially increases fit sensitivity.
Explicit 6+ years, 2+ years LLM experience, and multiple mandatory technical and leadership requirements.
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Own architecture, lifecycle, and operational delivery of AI agentic and retrieval systems within healthcare AI products.
Lead and grow a small engineering team while maintaining hands-on coding and technical leadership.
Define and execute quarterly roadmap, translate business problems into AI/ML solutions, and ensure deployment meets clinical and operational reliability and cost standards.
6+ years in data science, applied ML, or AI engineering with 2+ years building LLM-powered products.
Strong hands-on Python and experience with deep learning frameworks such as PyTorch and HuggingFace transformers.
Experience shipping complex GenAI products with multi-agent architectures, retrieval, memory, and observability in production.
Experience leading engineers or technical teams; Master’s degree preferred but not mandatory.
Experienced in operationalizing production-grade retrieval augmented generation (RAG) and LLM/SLM features end to end with minimal supervision.
Proficient in designing and tuning AI retrieval pipelines and agent orchestration involving memory, tool routing, and failure handling.
Skilled at balancing engineering trade-offs including latency, cost, accuracy; capable of interfacing between engineering teams and business/customers.