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Inside Higher Ed · 4 days ago

AI/LLM Developer/Engineer

Chapel Hill, NC

Full-time

Onsite

Mid, Senior Level

$26.04/hr - $33.85/hr

3+ years exp

Inside Higher Ed is seeking an AI/LLM Developer/Engineer to join the School of Nursing at the University of North Carolina at Chapel Hill. The role involves leveraging Large Language Models (LLMs) and clinical data analysis to contribute to innovative healthcare delivery improvement projects.

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Responsibilities

Design, fine-tune, and evaluate large language models (LLMs) tailored to domain-specific applications using techniques such as transfer learning, LoRA, and reinforcement learning with human feedback (RLHF)

Build intelligent applications powered by LLMs, including chatbots, virtual agents, clinical decision tools, or document analyzers, using frameworks like LangChain, LlamaIndex, or semantic search pipelines

Develop scalable LLM pipelines and infrastructure, including data ingestion, preprocessing, model serving (via GPU/TPU), and continuous performance monitoring

Integrate commercial and open-source LLMs (e.g., OpenAI GPT, Claude, Mistral, LLaMA) via APIs or local deployment into digital health or enterprise systems

Craft and iterate prompts using advanced prompt engineering and chain-of-thought strategies to improve output relevance, tone, factuality, and task completion

Implement retrieval-augmented generation (RAG) architectures to enhance context awareness using vector databases (e.g., Pinecone, FAISS, Weaviate)

Evaluate LLM performance using automated and human-in-the-loop methods to assess accuracy, hallucination, safety, and user satisfaction

Collaborate across disciplines with data scientists, UX designers, domain experts, and MLOps to ensure usability, performance, and alignment with real-world needs

Monitor and optimize system performance, including latency, throughput, token usage, and model cost-effectiveness across deployment environments

Stay current with advancements in generative AI, contributing to the internal knowledge base and driving adoption of best practices for ethical and responsible LLM use

Qualification

Natural Language ProcessingLarge Language ModelsPythonDeep Learning FrameworksRetrieval-Augmented GenerationClinical Data FamiliarityProblem-Solving SkillsCollaborative Environment