Director & Group Head (AI-Systems & Scale)

Responsibilities

• Define and lead the multi-year roadmap for scalable AI systems, platforms, and agentic architectures across research.
• Lead and scale multidisciplinary teams delivering production-grade AI systems and intelligent applications.
• Establish reference architectures for agentic systems, including orchestration, retrieval, memory, and human-in-the-loop controls.
• Build and standardize enterprise agentic platforms with reusable components, workflows, and evaluation frameworks.
• Define and enforce machine learning operations and large language model operations standards across AI systems development.
• Own agent quality and safety engineering, including guardrails, policy enforcement, and failure detection mechanisms.
• Drive scalable deployment strategies, ensuring performance, reliability, cost efficiency, and multi-tenant architecture.
• Define performance metrics linking system effectiveness to scientific outcomes, including accuracy, robustness, and efficiency.
• Partner with data, security, and platform teams to ensure governance, compliance, and responsible data usage.
• Drive cross-functional collaboration and portfolio management to prioritize high-impact AI system initiatives and reduce duplication.

Requirements

• Demonstrated experience leading machine learning capability development initiatives across multiple drug discovery domains and teams.
• Strong experience applying machine learning across functional areas such as computational chemistry or protein modeling.
• Proven expertise in large model training, including high-performance computing, cloud environments, and distributed systems.
• Deep curiosity and passion for biomedical sciences and therapeutic discovery.
• Strong ability to explain complex machine learning concepts clearly to both technical and non-technical stakeholders.
• Minimum of 10+ years of experience in innovation, development, deployment, and support of machine learning solutions.
• Strong coding proficiency in Python and deep learning frameworks, with experience using version control systems.
• Proven ability to manage complexity, align stakeholders, and deliver results in matrixed environments.

Nice to have

• Experience building enterprise-scale agentic platforms with orchestration, memory, and retrieval systems.
• Expertise in machine learning operations and large-scale model deployment with monitoring and drift detection.