LLM / GENAI Prototyping Specialist

You will help stand up and scale early-stage LLM and agent-based solutions as part of a Life Sciences Cloud transformation program. This role focuses on rapid prototyping and maturation of AI-powered assistants that support internal users through training, hypercare, and operational enablement—particularly over large volumes of unstructured content. You will translate ambiguous business needs into concrete LLM behaviors, design retrieval-augmented generation (RAG) solutions, and collaborate closely with training, product, field, and engineering stakeholders to deliver practical, high-impact AI capabilities. This is a contract role, remote-first, with NYC or Collegeville proximity as a nice-to-have. Job Responsibilities • Design, prototype, and iterate on LLM-powered assistants for training, hypercare, and operational enablement use cases • Convert loosely defined requests (e.g., "how-do-I assistant” or knowledge companion) into clear conversational flows, system prompts, and grounding strategies • Build and tune RAG pipelines over unstructured document sets, including: • Document chunking strategies • Metadata and tagging design • Vector search relevance tuning • Ensure retrieval logic, prompt design, and response behavior are tuned holistically • Take prototypes from proof-of-concept to more robust, enterprise-ready solutions by: • Designing evaluation datasets and test cases • Iterating to improve accuracy, consistency, and latency • Partner with training, product, and field stakeholders to operationalize content and prioritize high-value use cases • Collaborate effectively with platform and data engineers within an enterprise application ecosystem • Focus on pragmatic, "base-hit” AI use cases that measurably improve day-to-day workflows for end users Qualifications • 2–3+ years of hands-on experience with GPT-class models or equivalent LLMs • Strong expertise in prompt engineering, system prompt design, and grounding strategies • Practical experience building retrieval-augmented generation (RAG) systems over unstructured content • Clear understanding of how chunking, retrieval, and prompting interact as a system • Experience embedding LLM capabilities into enterprise applications (e.g., Salesforce, ServiceNow, Dynamics, SAP, or custom internal platforms) • Experience working in or adjacent to regulated environments (life sciences, healthcare, or financial services preferred) • Ability to operate independently in a fast-moving, prototype-driven environment • Strong communication skills and comfort working directly with non-technical stakeholders • Mid-to-senior level individual contributor profile Nice to Have • Familiarity with Salesforce AI capabilities, including AgentForce or Einstein • Prior work on training assistants, hypercare tools, or knowledge-based AI companions • Familiarity with AWS fundamentals (e.g., S3, Lambda) and data pipelines • Life sciences, pharma, or other highly regulated industry experience