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Mid-level metro role with some company brand and general ML appeal, but niche LLM requirements moderate applicant density.
Strong ML/LLM specialization transfers across industries, though healthcare domain knowledge is a preferred plus.
Explicit 6+ years, 2+ years LLM experience, mandatory stacks, and shipped GenAI product make filters very strict.
Own end-to-end architecture, deployment, and lifecycle of agentic and retrieval AI systems serving real customer scale and reliability.
Lead and grow a small engineering team with hands-on coding, code review, and quality ownership.
Define and execute AI product roadmap, working closely with business leaders and customers to build integrated AI workflows into healthcare products.
6+ years in data science, applied ML, or AI engineering with 2+ years building LLM-powered products.
Strong hands-on Python experience building scalable enterprise applications.
Experience with deep learning frameworks (PyTorch and/or HuggingFace transformers) and shipped GenAI product with complex architecture (multi-agent, memory, retrieval, production tracing).
Experience leading engineers or technical teams; Master's degree in CS, Computer Engineering, or related field preferred.
Expertise in applied NLP and Generative AI within production healthcare or data-heavy domains.
Experience designing and optimizing retrieval-augmented generation (RAG) pipelines, multi-agent orchestration, and LLMOps.
Demonstrated ability to translate ambiguous business/clinical problems into scalable AI workflows with measurable impact and to navigate engineering economics for build vs buy decisions.