Summary
Director, AI Systems Foundations & Reusable Capabilities #LI-Hybrid Location: London Novartis is unable to offer relocation support for this role: please only apply if this location is accessible for you. The Director, AI Systems Foundations & Reusable Capabilities is a senior technical AI leadership role within Data Science & AI, responsible for establishing the reusable foundations that enable Development AI systems to be built faster, more consistently, and with greater technical leverage across Novartis. The role identifies common technical patterns across AI initiatives and shapes the long-term evolution of Development's AI capability stack across agentic AI, retrieval and knowledge systems, simulation and digital twins, MLOps, LLMOps, AgentOps, and AI lifecycle management. It translates proven architectures, components, engineering practices, and design decisions into reusable AI capabilities that reduce duplication and ensure learning from one system strengthens the next. This role owns reusable AI system foundations and technical AI capability assets, not product roadmaps, business adoption, portfolio governance, infrastructure operations, or enterprise architecture. It partners closely with DSAI domain leaders, the AI Systems Reliability Director, Product teams, Strategy & Governance, and DDIT to ensure AI capabilities are reusable, interoperable, technically coherent, and continuously improved.
About the Role
Major Accountabilities
AI Foundations & Reference Architectures
- Define reusable architectures, design patterns, and reference implementations for AI systems across Development.
- Establish common approaches for agentic AI, retrieval-augmented generation, knowledge systems, simulation, digital twins, and human-in-the-loop AI.
- Provide technical guidance on reuse, extension, and evolution of AI capabilities.
- Ensure AI systems are designed for modularity, interoperability, scalability, and maintainability.
Reusable AI Capabilities & Engineering Practices
- Build and maintain reusable AI components, frameworks, templates, and capability assets.
- Define AI engineering practices and reference approaches for MLOps, LLMOps, and AgentOps.
- Establish standards for lifecycle management, versioning, orchestration, observability, and technical documentation.
- Identify recurring technical needs and convert them into reusable organizational capabilities.
Capability Scaling & Technical Enablement
- Partner with domain leaders to identify successful patterns that can be reused across Development.
- Translate project-level solutions into reusable architectures, components, and implementation blueprints.
- Guide AI engineers and data scientists in designing systems that can be reused and extended across domains.
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Director, AI Systems Foundations & Reusable Capabilities - Novartis employer: OpenTalent
At TwinStream, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation. Our hybrid working model in Cheltenham allows for flexibility while providing ample opportunities for professional growth and development within the tech sector. With a focus on meaningful projects that support government organisations, employees can find purpose in their work while enjoying competitive benefits and a supportive team environment.
