Introduction to the role

Help shape and scale a multi-cloud AI platform that speeds up the discovery and development of life-changing medicines This role leads the strategy, build and evolution of enterprise compute and cloud-native AI services used every day by scientists and engineers across the business.

The focus is clear: turn ambitious ideas into secure, compliant and reusable capabilities that work at scale. Success in this role means connecting data foundations, AI technologies and smarter ways of working into one strong platform that helps teams move faster, with less friction and more confidence. It is a chance to set direction, remove blockers and drive adoption across a general organisation

What this role will do

This role will define how AI and machine learning services are used across AWS, Azure and GCP , creating a simpler and more consistent experience across multiple cloud environments. Managed services such as SageMaker, Azure ML and Vertex AI will be brought together through strong architecture, Infrastructure as Code and practical integrations that make secure adoption easier.

Enterprise compute strategy will sit at the heart of the role, balancing performance, cost, resilience and compliance . The platform will need repeatable, auditable Terraform foundations that support AI services with the rigour required in regulated environments.

The role will also unify cloud-native AI services into a more consistent platform model, with shared patterns, governance and guardrails that reduce duplication and support faster delivery. Rather than leaving teams to reinvent solutions, the platform will provide dependable standards that teams can adopt and scale.

Leadership is a major part of the remit.



A high-performing team of AI platform and DevOps specialists will be built, coached and challenged to raise the bar. An automation-first mindset, strong engineering discipline and compliance-by-design approach will be central to how the team operates.

The role also carries ownership for budgets and resourcing across headcount, cloud consumption, outsourced services and specialist partners . Strong FinOps thinking will be important to improve visibility, control spend and maximise value.

Alongside delivery, this role will champion enterprise-wide adoption. Proven platform patterns, reusable components and successful ideas will be scaled across functions and geographies, helping more teams work with greater speed, safety and efficiency.

Close partnership with leaders across R&D;, engineering, security and compliance will be essential. The role will translate scientific and business priorities into technical strategy, practical roadmaps and measurable outcomes.

Essential Skills/Experience

- We’re looking for someone with 15+ years of experience, including at least 5 years in senior technical leadership roles focused on AI platforms, cloud infrastructure, or cloud-native AI services, ideally in regulated industries.



This person will bring a strong track record of leading large-scale platform transformations and helping teams navigate complex, multi-year change with clarity and confidence.
- Deep expertise across AWS, Azure and GCP is expected, especially in AI and machine learning services. Strong hands-on knowledge of Terraform is also important, including enterprise module design, multi-cloud implementation, CI/CD integration, and state management and governance.
- This role calls for broad compute leadership experience, including Kubernetes, serverless and event-driven architectures, GPU optimisation for machine learning workloads, auto-scaling and cost optimisation . A strong engineering background with hands-on capability in Python, Bash and PowerShell will be valuable.
- Experience in GxP-compliant systems is important, ideally in life sciences or pharmaceutical settings, including documentation and validation requirements. Strength in DevOps and GitOps tooling is also needed, covering areas such as Git, GitHub Actions, Azure DevOps, Docker and Kubernetes .
- A solid grasp of cloud security, identity and access management, and data governance across multi-cloud environments is essential. Strong communication and stakeholder leadership skills matter just as much, especially the ability to influence across a large global organisation.

Desirable Skills/Experience

- Experience with MLOps practices and platforms such as MLflow, Kubeflow and feature stores would be useful, as would knowledge of observability tooling including Datadog, Prometheus, Grafana and CloudWatch .
- Background in FinOps, large-scale data pipelines, pharmaceutical R&D; workflows, scientific computing , or co

📌 Senior Director, AI Cloud (España)
🏢 AstraZeneca
📍 España