AI/LLM Developer/Engineer

• 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.

• Expertise in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), deep learning frameworks.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, or LangChain
• Familiarity with clinical or healthcare data (e.g., EHRs, clinical notes, structured claims data)
• Proven research record with peer-reviewed publications in relevant fields
• Strong problem-solving skills and the ability to work in a collaborative environment.

• Distributed parallel training and parameter-efficient tuning.
• Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering.
• Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG)
• Experience deploying large-scale machine learning models in cloud environments.

Not Applicable.