LLM / AI Prototyping Specialist

Key Responsibilities

• Design, build, and iterate rapid prototypes of LLM-powered assistants using retrieval-augmented generation (RAG) over large volumes of unstructured content (PDFs, SOPs, training materials)
• Architect end-to-end RAG pipelines: document chunking strategies, metadata/tagging design, vector search tuning, and holistic prompt-retrieval-response optimization
• Translate loosely defined requests into precise system prompts, grounding logic, and evaluation criteria
• Mature prototypes from proof-of-concept to enterprise-ready solutions through rigorous testing, accuracy tuning, and latency optimization
• Partner directly with training, product, field, and engineering stakeholders to prioritize high-value use cases and operationalize content
• Embed AI capabilities into enterprise platforms (Salesforce, ServiceNow, or custom internal tools) within a regulated life sciences environment

Required Qualifications

• 2 3+ years of hands-on experience building and shipping LLM/RAG prototypes into real-world use (not academic/research projects only)
• Demonstrable expertise in prompt engineering, system prompt design, and grounding strategies for unstructured content
• Practical experience tuning the full RAG stack: chunking embedding retrieval generation evaluation
• Track record embedding LLM capabilities into enterprise applications (Salesforce, ServiceNow, Dynamics, SAP, or custom platforms)
• Comfort operating independently in fast-moving, prototype-driven environments with ambiguous requirements
• Ability to clearly explain technical tradeoffs to non-technical stakeholders

Preferred Qualifications

• Experience in regulated industries (life sciences, pharma, healthcare, or financial services)
• Familiarity with Salesforce AI (AgentForce, Einstein) or similar enterprise AI platforms
• Prior work on training assistants, hypercare tools, or knowledge companions
• AWS fundamentals (S3, Lambda) and data pipeline exposure
• Pharma or life sciences domain knowledge