AI Director, Development Global Clinical Operations

Job Purpose

As AI Director, you will drive the AI-native Development Operations strategy: defining where AI can create meaningful value across Development Operations, building working prototypes hands-on to prove it, and working with enterprise AI and technology teams to take priority capabilities from prototype into production and scaled deployment. You will work directly with business SMEs to build and pressure-test AI-enabled workflows. Where external capability is needed, you will lead vendor evaluation and shape build-buy-partner decisions with commercial rigour. This role combines strategy, hands-on solution building, and deployment judgement. It requires structured problem-solving, strong commercial and enterprise credibility, and technical depth in building agentic AI. This role reports into the Head of Asset Acceleration and AI.

Key Responsibilities

• Drive the AI-native Development Operations strategy and roadmap, anchored to measurable clinical trial outcomes.
• Lead 0-to-1 incubation: frame hypotheses, build working AI prototypes hands-on on real workflows, and define the evidence to scale, reshape or stop.
• Design and build AI systems from the ground up, then oversee handover and production deployment with technology teams: solution architecture, evaluation, regression testing, observability, guardrails, security controls and GxP validation.
• Lead prioritisation of AI opportunities with leaders across R&D: identify priority workflow bottlenecks, define measurable outcomes, and drive alignment behind the AI-native strategy.
• Define how AI capabilities are built: evaluations, model choice, agentic system structure, and shared reusable components, then deliver them with enterprise technology teams.
• Define the build-buy-partner approach for each priority capability, including vendor evaluation, balancing speed, differentiation, risk, cost and long-term platform value.
• Build and defend business cases with clear ROI, payback logic and success criteria; enforce stage-gates, including stopping or reshaping initiatives that do not deliver.
• Redesign the workflow around each priority capability with business SMEs and frontline users so that deployment changes how the work is done and value is realised at scale.
• Scan the landscape for emerging AI capabilities, frontier tools and vendor offerings, translating them into implications for strategy, priorities and build-buy-partner choices.
• Manage stakeholders across R&D leaders, enterprise AI/technology/security/quality teams, external vendors, and frontline users across Development Operations.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, Life Sciences, or a related field.
• Strong experience in applied AI/technology or Pharma strategy, gained at a leading AI or technology consultancy, the AI practice of a top-tier strategy consultancy, or a comparable applied-AI environment, including hands-on design and build of AI solutions.
• Technical depth in how agentic AI systems are built and where they break, including retrieval and context, tool use, orchestration, evaluation and reliability.
• Able to code in Python and SQL, with working knowledge of LLM APIs, embeddings, agent frameworks, MCP, data pipelines and cloud platforms (Azure, AWS).
• Deep understanding of clinical operations and wider clinical development, gained in industry, consulting or other relevant settings.
• Strong knowledge of industry trends and technologies shaping the future of clinical trials.
• Demonstrated experience building business cases and delivering AI-enabled transformation programmes with measurable business outcomes.
• Hands-on experience building solutions leveraging frontier agentic development tools (e.g. Claude Code, Codex).
• Strong commercial judgement and experience shaping enterprise vendor decisions.
• Exceptional written and verbal communication, with the ability to translate complex strategic and technical topics for senior, non-technical audiences.

Preferred Qualifications

• Advanced degree (PhD, MBA, MSc, or equivalent) in a relevant discipline.
• Experience taking AI from prototype into production with enterprise technology teams.
• A record of self-initiated builds outside the day job, such as agents, tools or prototypes.
• Track record of taking AI beyond pilot to scaled adoption and portfolio-level impact.
• Broad knowledge of Pharma R&D processes and the regulatory, scientific and operational challenges unique to the sector.
• Knowledge of GxP and compliance frameworks for deploying AI in clinical operations.