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: Metadata and tagging design 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 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 #J-18808-Ljbffr Experience: 3 years required