Dr. Pouyan R. Fard

- Cross-team AI/ML Staff Engineer/Architect, responsible for AI/ML architecture, MLOps standards, and high-impact solution design. - Defined scalable, compliant ML and MLOps reference architectures on Azure and Databricks. - Owned internal LLM APIs and GenAI platforms; led ADRs and solution reviews. - Designed LLM apps and agents - Mentored data science teams - Drove observability and cost optimization; - Led PoCs in clinical and medical-device contexts; and evaluated vendors for architectural alignment.

- Founder and Lead AI Architect delivering end-to-end AI and data solutions for global enterprises. - Led ML, NLP, and LLM systems across healthcare, pharma, life sciences, and industrial clients (e.g., Boehringer Ingelheim, Sartorius, BASF, DSM). - Owned delivery from problem framing to handover, led client-facing architecture, managed multi-disciplinary teams, drove consulting revenue, delivered industry talks, oversaw business operations, and founded AI and HR-tech spin-offs.

Worked in the Applied Intelligence practice, supporting enterprise AI, analytics, and data initiatives for regulated and industrial clients in the DACH region. - Contributed to AI and data architecture for global pharma and life sciences clients - Supported client-facing solution design and business analysis - Contributed to RFPs and a winning commercial proposal - Built a foundation in enterprise consulting and data-driven strategy

Held a teaching and research role in applied data science, machine learning, and computational neuroscience in a life-sciences and medical research environment. - Published 5+ peer-reviewed journal and conference papers in computational neuroscience - Taught data science, ML, and statistics to 150+ graduate students - Supervised junior researchers and students in data collection and ML analysis - Built predictive models of human decision-making using behavioral and neuroimaging data

Early-career role in medical technology research, applying ML, data analysis, and digital technologies in clinical and biomedical engineering contexts. - Built AI- and data-driven prototypes for motion analysis and rehabilitation - Developed processing and visualization frameworks for motion-capture and physiological data - Collaborated with clinicians, engineers, and researchers across disciplines - Contributed to applied research forming the basis of a Master’s thesis in neural information processing