AI Data Architecture

Main Responsibilities • Define and lead the enterprise data architecture strategy to enable scalable, secure, and high-quality AI/ML applications across R&D, clinical, manufacturing, and commercial functions. • Design and maintain data platforms, data models, and integration frameworks that support structured, unstructured, and real-time data for AI use cases. • Establish standards for data ingestion, transformation, storage, metadata management, lineage, and interoperability across enterprise systems. • Partner with business, digital, and analytics teams to ensure data architecture aligns with AI product roadmaps and enterprise priorities. • Build data pipelines and architecture patterns that support advanced analytics, generative AI, MLOps, and knowledge management solutions. • Ensure compliance with security, privacy, governance, and regulatory requirements related to GxP, patient data, and enterprise risk management. • Drive adoption of modern cloud-based data architecture, including data lakes, warehouses, APIs, semantic layers, and master data management. • Lead architecture reviews and collaborate with vendors and internal stakeholders on technology selection, implementation, and optimization Required Skills • 3+ years of experience in data architecture, enterprise architecture, data engineering, or AI platform design, preferably in pharma/life sciences. • Proven experience designing enterprise-scale data environments that support analytics and AI applications. • Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field. • Strong expertise in cloud platforms (AWS, Azure), data lake/warehouse architecture, ETL/ELT, API integration, and distributed data systems. • Hands-on experience with data governance, master data management, metadata frameworks, and data quality controls. • Familiarity with AI/ML infrastructure, MLOps, vector databases, and modern data stack technologies. • Understanding of pharmaceutical enterprise systems and data domains, including clinical, regulatory, R&D, ERP, CRM, and manufacturing systems. • Excellent leadership, architecture governance, communication, and stakeholder management capabilities.