Sebastian Schickhoff
Lead Solution Engineer Life Sciences @Salesforce
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WORK HISTORY
Lead Solution Engineer Life Sciences @Salesforce
Munich, DE
Orchestrating global solution architecture for Top 10 DACH Pharma enterprises, driving the migration from legacy incumbents (Veeva) to Salesforce Life Sciences Cloud across Commercial and Medical Affairs.• : Developing custom, GxP-compliant AI agents using Python (LangChain, OpenAI, Anthropic API) to bridge product gaps. Engineering deterministic evaluation loops to validate Salesforce Agentforce outputs against industry standards (e.g. 21 CFR Part 11, EU AI Act) for regulated pilots.• : Architecting high-volume Salesforce Data 360 implementations to unify siloed clinical and commercial data, designing real-time ingestion pipelines that feed a grounded RAG context window to reduce hallucination risks.
EDUCATION
University of Glasgow
Bachelor of Science with Honours
St. Michaels University School
British Columbia Certificate of Graduation (Abitur-equivalent)
ABOUT SEBASTIAN SCHICKHOFF
With a background in Neuroscience and Statistics, I view AI not as magic, but as a complex system that requires rigorous evaluation. I currently serve as the Lead Solution Engineer for Salesforce’s largest DACH Life Sciences accounts, orchestrating the transition from legacy CRM systems to autonomous, agentic workflows.My work focuses on the last mile of enterprise AI, solving engineering issues that standard tools cannot address. I build and deploy GxP-compliant prototypes using Python, LangChain, and Salesforce Agentforce to demonstrate that large language models can operate safely within strict medical, legal, and regulatory (MLR) boundaries: Leading the technical AI roadmap for Top 10 Global Pharma companies : Prototyping deterministic evaluation loops and RAG architectures to mitigate hallucination risks : Translating 21 CFR Part 11 and EU data residency constraints into technical guardrails :🤖 : OpenAI / Anthropic API, Vector Search (RAG), Salesforce Agentforce : GxP, Clinical Data Models, Commercial Pharma Strategy : Python (Pandas / NumPy), SQL, Unstructured Data Strategy
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