Introduction to role:

Are you ready to design the enterprise AI backbone that powers faster science and smarter operations? In this senior leadership role, you will define and scale platform and enabling architectures that turn agentic AI, foundation models, and interoperable context management into measurable outcomes—accelerating decisions in R&D and across the enterprise while meeting the highest standards of data governance and regulatory compliance.

You will develop the roadmap and reference architectures applicable to open-source and also proprietary ecosystems. You will collaborate with teams using Amazon Q, Amazon Bedrock, SageMaker, OpenAI, Databricks, and other new technologies. Your work will connect the dots across domains, simplify a complex landscape, and increase the agility of critical business processes—driving efficiencies, reducing risk, and unlocking value at scale.

Can you translate brand new AI into secure, scalable platforms that speed delivery of life-changing medicines while improving the way we operate every day? If you thrive on uniting diverse experts to tackle complex challenges and make decisive progress, this is your opportunity to lead.

• Enterprise AI Strategy: Define and evolve the organizational AI architecture strategy aligned with scientific and business objectives, ensuring platforms and products deliver tangible value.
• Agentic AI and Large-scale Models: Lead architecture build for multi-agent systems and foundational AI models (LLMs, multimodal), enabling resilient, observable, and governable solutions.
• Reference Frameworks and Protocols: Establish reusable blueprints and standards for platforms across both open-access and licensed environments to accelerate safe adoption and scale.
• Interoperability via MCP: Drive adoption of the Model Context Protocol for consistent context management and interoperability across tools and services.
• Scalable Platforms on Cloud and Mixed Environments: Develop cloud and hybrid environment AI platforms with AWS, OpenAI, Databricks, and related services to improve enterprise throughput, reliability, and cost efficiency.
• AI Lifecycle Enablement: Enable the full AI lifecycle—from discovery and MVP to productionization, optimization, and retirement—backed by clear SLOs and feedback loops.
• Advanced AI Capabilities: Design and guide implementation of RAG patterns, vector databases, knowledge systems, and the data pipelines they depend on.
• Responsible and Secure AI: Embed governance, compliance, and risk management, anticipating threats such as data poisoning, model theft, and adversarial attacks, and translating regulations into actionable controls.
• Cross-Enterprise Alignment: Partner with product, data governance, security, engineering, and business leaders to align AI initiatives and accelerate high-value use cases.
• Leadership and Mentorship: Build and mentor high-performing enterprise and solution architecture teams, developing skills, career paths, and delivery excellence.
• Value Acceleration: Identify, assess, and prioritize use cases with business stakeholders; translate strategy into practical solutions and constructively challenge low-value or misaligned initiatives.
• Business-Driven Delivery: Gather insights from users, data scientists, engineers, and operations to align delivery with current and future needs, turning them into scalable, reliable processes.
• Technology Selection and Integration: Select fit-for-purpose technologies across open-source and commercial platforms, recommending cloud, on-premises, or hybrid deployment models and ensuring seamless integration with data and analytics ecosystems.
• Continuous Improvement and MLOps: Evaluate tools and practices across data, models, and software engineering; set up feedback mechanisms for service performance, model recalibration, and retraining.
• ML/AI Pipelines: Guide pipeline architecture decisions across data management, governance, model development, deployment, and production operations, with clear trade-off reasoning.
• Modern Engineering: Apply strong software engineering and DevOps principles, including Git, containers, Kubernetes, and CI/CD, to increase speed and reliability.
• Applied Data Science Understanding: Work fluently with analytics and ML concepts and tooling (e.g., SAS, R, Python, TensorFlow, ensembles, neural networks) to bridge architecture and data science practices.
• Executive Thought Leadership: Act as a change agent and trusted advisor; communicate opportunities, limitations, and risks of AI to senior stakeholders and influence decision-making.
• Enterprise Collaboration: Build strong partnerships across data science, engineering, architecture, and executive leadership to align around shared outcomes.
• Information Architecture Ownership: Deliver conceptual and logical models for operational, master, and data products; define information flows, master and reference data, and metadata to meet capability needs.
• Strategy-Evolving IA: Work with business leaders to evolve information architecture in line with strategy and capability roadmaps.
• Domain and Transformation Leadership: Own AI designs for large or complex capability domains and take accountability for enterprise architecture across major transformation programs.
• Design Assurance and Alignment: Create enterprise architecture blueprints and review project designs to ensure alignment with target architectures and standards.
• Embedded Data Governance: Partner with Data Offices to embed governance with measurable controls (e.g., master data consumption, classification metadata) across designs and access processes.
• Patterns for Analytics and Operations: Select and define architectures and patterns for reporting, analytics, data science, digital, and operational use cases.
• Strategic and Tactical Support: Provide planning, design expertise, and delivery support across technology standards, models, and enterprise architecture considerations.
• Integration Architecture: Support or lead AI integration architecture and end-to-end data integration design for complex initiatives.
• IA Governance and Standards: Secure approval for IA artifacts and enforce standard enterprise data element names, abbreviations, characteristics, and domains throughout the lifecycle.
• Resource and Work Package Management: Define and manage work packages for internal and flexible resources, ensuring clarity of outcomes and accountability.
• Demand and Financials: Manage demand planning and recharge activities for AI and technology programs in partnership with practice leaders and project managers.

• Bachelor’s degree, or equivalent experience, in data science, AI engineering, or a related field.
• A seasoned professional with 15+ years of experience, anchored by extensive leadership in AI platform strategy. Consistently transforms theoretical architectural models into robust, real-world platforms.
• Experience with conceptual and logical data modelling techniques and tools.
• Experience defining and applying information and data governance standards in regulated environments.
• Brings a strong mix of data and information architecture, analysis, and engineering expertise.
• Experience with established IT architecture patterns, methodologies, and AI platforms such as Amazon Bedrock, Amazon Q, SageMaker, Azure AI, and Google Cloud AI.
• Strong understanding of AI platform concepts and cloud-based containerization strategies for hybrid environments.
• Able to select the right AI architecture and technologies based on business use cases, with a strong understanding of the full AI lifecycle.
• Lead a small team of AI architects and help shape AI strategy and direction across the enterprise landscape.

• Postgraduate degree in MIS, AI, data science, or a related field.
• Ability to lead a small team of AI enterprise architects while guiding and supporting multiple projects simultaneously.
• Recognized thought leader in applying AI within the enterprise and across the industry.
• Extensive experience in senior AI, data science, data engineering, and AI architecture roles, with a strong track record of designing and delivering end-to-end and point architecture blueprints for large-scale, real-world use cases.
• Experience applying AI and data governance frameworks within a commercial organization.
• Experience working in Agile AI delivery and definition scrums.
• Experience using tools such as metadata cataloguing, data modelling, and enterprise architecture platforms.
• Hands-on experience building AI models, including LLMs and LVMs, working across diverse data types to deliver accurate and reliable outcomes.
• Experience working in the pharmaceutical AI industry.
• Experience in AI architecture, pipeline planning, and deploying production workflows for AI models.
• Ability to simplify the enterprise landscape and reduce technology debt.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

Lead the architecture that transforms breakthrough AI into enterprise-wide value—step in and make your impact count.

If you are ready to lead enterprise-scale AI that delivers measurable business value and meaningful patient impact, step forward and show us how you will make it real. Apply today.

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