AI Engineering & Data Science Lead

Role Purpose An AI Engineering & Data Science lead is for an AI Engineering and Data Science capability within the Lonza AI Factory (LAIF), defining the engineering standards, reusable capabilities, and technical approaches required to build and scale AI solutions across Lonza. This role is responsible for shaping the AI intelligence layer, driving AI engineering excellence, and enabling the successful delivery of AI products through cross-functional Fusion Teams. The position ensures AI solutions are scalable, secure, compliant, and designed for enterprise-wide reuse and adoption. Key Responsibilities AI Engineering Strategy, Architecture & Reusable Capabilities Define the AI engineering and data science strategy, architecture principles, and technical roadmap for the Lonza AI Factory. Shape and evolve the AI intelligence layer, including reusable services, contextual frameworks, orchestration capabilities, agentic patterns, and shared AI services. Establish engineering standards and reference architectures covering AI/ML development, evaluation, deployment, monitoring, and lifecycle management. Own and continuously evolve the approved catalogue of AI models, algorithms, frameworks, tools, and reusable components. Partner with DDAI, IT, and technology teams to define technical requirements and priorities for AI platform services. Drive build-versus-buy-versus-partner decisions for AI technologies and continuously evaluate emerging technologies and market trends. AI Engineering & Data Science Delivery Lead AI engineering and data science delivery for strategic AI products and business-critical use cases through cross-functional Fusion Teams. Provide hands-on technical leadership for complex AI solutions, including Generative AI, Agentic AI, Machine Learning, and Advanced Analytics. Drive the industrialization of AI solutions by transitioning initiatives from proof-of-concept and experimentation into enterprise-scale production deployments. Establish robust evaluation, testing, validation, performance measurement, and continuous improvement frameworks for AI models and agents. Promote the reuse of common AI capabilities, engineering patterns, and shared services to maximize efficiency and scalability across products. Support AI Product Leads and Business Product Owners in translating business challenges into technically viable and impactful AI solutions. AI Lifecycle, Operations & Responsible Engineering Define AI and Machine Learning engineering lifecycle standards in partnership with IT and DDAI, including deployment, observability, monitoring, versioning, model governance, and operational resilience. Embed security, privacy, responsible AI principles, and regulatory requirements into engineering frameworks and development practices. Establish technical controls for AI evaluation, traceability, explainability, human oversight, and evidence generation, particularly for regulated and GxP-relevant environments.