Senior AI Platform Engineer

Introduction to role The AI Cloud & Compute Services team, part of AstraZeneca's Enterprise AI unit , is responsible for orchestrating the compute strategy that underpins AstraZeneca's ambition to use AI in every step of the value chain – from discovering new compounds to patient safety systems. We focus on understanding what AstraZeneca truly needs for AI compute, ensuring that the most sustainable and cost-efficient options are leveraged across a multi-cloud landscape that is compliant by design. We are looking for a Senior AI Platform Engineer to join our AI Cloud & Compute Services team. This is a technical leadership role focused on driving AI cloud and compute strategy - planning, provisioning, and optimisation across clouds and environments for cost-efficient scale. The ideal candidate will have industry-relevant experience delivering scalable AI/ML infrastructure and cloud computing services, with deep expertise across AWS, Azure, GCP, or equivalent platforms. You will be part of a collaborative, multidisciplinary team with the opportunity to shape how AstraZeneca provisions and operates AI compute at enterprise scale, directly enabling major AI initiatives such as clinical trial data analysis, knowledge graph analytics, patient safety systems, deep learning- led drug discovery, and software as a medical device systems . As an Senior AI Platform Engineer with a passion for building complex, scalable systems, you will act as a key technical leader – driving small teams or projects, shaping AI compute and platform strategy, mentoring junior colleagues, and translating complex infrastructure challenges into actionable, high-impact solutions for the business. Accountabilities Multi-Cloud AI Services – Design and deliver multi-cloud AI compute services that are compliant by design, ensuring security, governance, and regulatory requirements are embedded from the outset. AI Compute Capacity Strategy – Orchestrate AI compute capacity planning, provisioning, and optimisation across multiple clouds and environments to deliver cost-efficient, sustainable scale. Sustainability & Cost Optimisation – Evaluate and recommend compute strategies that balance performance with sustainability and cost-effectiveness. AI/ML Ops Embedding – Drive the standardisation of AI/ML deployment, monitoring, and lifecycle management across platforms, ensuring robust MLOps practices are embedded in all services. Product Mindset – Operate with a product mindset: define clear roadmaps, SLAs, support models, and enablement programmes to drive adoption of compute services across business-facing teams. Operational Simplification – Remove operational complexity so that delivery teams can focus on innovation rather than maintenance; ensure compliance-ready operations by design. Technical Leadership & Mentoring – Act as a key technical leader, advising on best practices and innovative approaches, mentoring junior engineers, and shaping the strategic direction of AI cloud and compute services. Stakeholder Collaboration – Collaborate with Data Scientists, Machine Learning Engineers, and platform teams across the company to understand their compute needs and deliver infrastructure that underpins their research and production workloads. Governance & Compliance – Work closely with internal governance and compliance functions such as Cyber Security and Data Privacy to secure the compute estate without obstructing end-user productivity. Essential Skills/Experience B Sc/MSc/Ph.D. degree in Computer Science or a related quantitative or analytical field. Experience as a founder, entrepreneur, or experienced consultant will be considered equally. Significant demonstrable experience working with AWS, Azure, GCP , or similar multi-cloud environments at enterprise scale. Deep expertise in AI/ML compute infrastructure – including GPU provisioning, distributed training environments, and high-performance computing for AI workloads. Demonstrable experience with Infrastructure as Code (Terraform, CloudFormation, or equivalent) for deploying and managing AI/ML infrastructure at scale. Strong experience with MLOps practices – building pipelines to accelerate and automate model deployment, monitoring, and lifecycle management. Experience with cost management and optimisation of cloud compute resources, including FinOps principles and sustainability-focused compute strategies. Experience operating with a product mindset : defining roadmaps, SLAs, and support models for platform/infrastructure services. Experience using DevOps to enable automation strategies and reduce operational complexity. Experience working with internal security standards and framew