Senior Leader, AI Enablement
The Position
We are seeking an experienced senior leader of AI Enablement to join a Computational Innovation AI function (@computationalinnovation) that will deliver next generation foundational AI capabilities to support the discovery and development of innovative medicines.
A core component of the AI function is AI Enablement, the technical backbone enabling AI scientists and ML engineers to design, train, adapt and deploy models efficiently. The team will collaborate closely with Data Excellence and IT Computational Innovation to build the AI infrastructure, tooling and data foundations that allow AI Systems, AI Reasoning and Applied Analytics and AI teams to move rapidly from research to production, ensuring that cutting-edge AI approaches can be reliably translated into portfolio impact.
In this role, you will hire and lead a team of world-class AI infrastructure, data and MLOps engineers who remove technical barriers between researchers and their best work. From large-scale distributed/federated training and experiment tracking to model deployment, lifecycle management and cost optimisation, this role is critical to increasing research velocity and enabling delivery of production-quality AI capabilities across the drug discovery pipeline.
The successful candidate will be part of the senior leadership team and play a key role in shaping the AI strategy and delivering on the mission. This is a unique opportunity to be part of a critical strategic initiative for a pharmaceutical company that invests heavily in research and development to discover and develop innovative therapies that can improve and extend lives in areas of high unmet medical need.
Key Responsibilities
• Grow and lead a team of AI infrastructure, data and MLOps engineers enabling AI Systems, AI Reasoning and Applied Analytics and AI teams
• Contribute to building and running the AI function, defining AI strategy and ensuring success through delivery of AI Enablement capabilities that align with AI function goals and translate to portfolio impact
• Design, build and operate AI infrastructure for large-scale foundation model training, fine-tuning and deployment, including distributed training, federated learning, experiment tracking and model versioning
• Establish and promote (ML) engineering and MLOps best practice across Computational Innovation
• Partner with Applied Analytics and AI, Data Excellence and ITCI to ensure data acquisition, integration, provisioning and access support federated learning and multi-modal model development and deployment
• Balance effective use of existing enterprise infrastructure with development of large-scale model building capabilities required to support frontier AI work, including federated learning
• Own governance for AI infrastructure, vendor selection and cost optimisation, ensuring scalable, secure and sustainable platforms
• Build and leverage partnerships with third-party technology providers and
• collaborators as key force multipliers
Requirements
• MS or PhD (preferred) in Computer Science, Engineering or a related field
• Experience setting technical strategy and translating strategy into execution with the necessary infrastructure and resources in place
• Experience building and leading high-performing AI infrastructure, MLOps or ML platform teams
• Deep expertise in distributed training, federated learning, ML infrastructure and production-grade AI systems
• Strong software engineering background with experience in cloud platforms, container orchestration and ML workflow tooling
• Track record of enabling research teams to move faster through effective platforms, tooling and service-oriented delivery
• Ability to explain complex technical concepts clearly to scientists, engineers and senior leadership
• Strong writing skills for both technical documentation and internal communication
• Comfortable presenting to technical and non-technical audiences