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Mid-senior GenAI lead in metro with broad technical requirements and common title, increasing competition.
Specialized GenAI production and healthcare context increases domain sensitivity, though skills are partially transferable.
Many explicit technical must-haves: 6+ years, LLM product experience, PyTorch/HuggingFace, LoRA, ML platform.
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Own the end-to-end architecture, deployment, and lifecycle management of AI systems including agentic and retrieval augmented generation (RAG) systems with production-scale reliability and cost optimization.
Lead and grow a small engineering team building complex LLM-powered products involving multi-agent coordination, memory, retrieval pipelines, and operational evaluation pipelines.
Define and execute quarterly AI roadmap, collaborate directly with business stakeholders, and ensure AI capabilities integrate seamlessly into healthcare product workflows.
6+ years in data science, applied ML, or AI engineering, with 2+ years building LLM-powered products.
Hands-on Python and deep learning framework experience (PyTorch and/or HuggingFace transformers).
Experience with multi-agent LLM architectures, retrieval systems, fine-tuning methods (PEFT, LoRA, QLoRA), and ML platforms like Databricks, Azure ML, or SageMaker.
Experience leading engineers or technical teams; Master's degree in CS, CE, or related field preferred.
Experienced builder and operator of complex, production-grade AI systems at healthcare or enterprise scale, familiar with orchestration frameworks and evaluation methodologies.
Comfortable balancing engineering economics (latency, cost, accuracy) and making strategic build-or-buy decisions with measurable impact.
Ability to translate ambiguous business problems into measurable ML solutions and engage deeply with customers and cross-functional teams.