BASIC QUALIFICATIONSBachelor’s degree in Engineering, Computer Science, Information Systems, or equivalent experience.
Experience delivering digital or AI solutions in a manufacturing or regulated environment (pharma/biotech, medical devices, or similar).
Experience leading delivery teams or workstreams, including coordinating engineers, partners, and SMEs, with the ability to coach and develop others.
Solid product and project management skills – managing backlogs, priorities, stakeholders, and delivery outcomes end-to-end.
Hands-on experience building with AI/LLM technologies (e.g., prompt engineering, retrieval/RAG, agent frameworks, evaluation and guardrails) and translating user needs into working solutions.
Experience operating within formal quality systems and change control, with strong documentation discipline.
Excellent communication skills and ability to influence at multiple levels, including site leadership and global partners.
Entrepreneurial, hands-on approach: bias for action, comfort with ambiguity, and a track record of taking ideas from concept through prototype to adoption.
PREFERRED QUALIFICATIONSHons Degree or equivalent advanced qualification.
Hands-on ability to use AI and agentic tools to rapidly mock designs, prototype agent experiences, and co-create solutions directly with customers and manufacturing SMEs.
Working knowledge of OT/automation and manufacturing digital systems (e.g., MES, SCADA, historians, LIMS) and integration patterns.
Experience with validated system delivery approaches and regulated computer system validation (CSV) / assurance practices.
Experience with cloud data platforms and advanced analytics/AI in manufacturing contexts.
Formal certification (Agile/Scrum Product Owner, PMP/PRINCE2, ITIL, Lean Six Sigma) or demonstrated equivalent practice.
Experience leading global, multi-site programs and driving standardization/common platforms.
Financial management experience, including vendor governance, forecasting, and chargeback/showback models.
Hands-on experience delivering and operating AI solutions, including MLOps/LLMOps patterns (deployment, monitoring, evaluation) and/or agentic AI orchestration (tools, workflows, guardrails) in an enterprise environment.
Experience with responsible AI practices (risk assessments, bias/robustness testing, human oversight, documentation) and applying them within regulated or validated contexts.
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