AI Platform Engineer in London

The Senior AI Platform Engineer is responsible for defining and delivering the infrastructure strategy underpinning IQVIA's Large Language Model (LLM) programmes. This role provides technical leadership across compute, data, model lifecycle management, evaluation frameworks, and platform engineering, ensuring research innovations can be successfully transformed into secure, scalable, and production-ready AI solutions.

Acting as a key technical leader and cross-functional integrator, the Senior AI Platform Engineer partners with research, product, infrastructure, data engineering, and MLOps teams to design and operate the platforms required to train, evaluate, deploy, and govern large-scale AI systems across IQVIA products and healthcare use cases.

Key Responsibilities

• Own the AI platform and infrastructure roadmap, leading the planning and execution of LLM initiatives and translating research requirements into scalable engineering solutions.
• Partner with centralised infrastructure teams to design and deliver high-performance compute environments across AWS and on-premises platforms, including GPU infrastructure, Slurm clusters, and migration from ad hoc research workflows.
• Optimise LLM training and inference workloads, supporting research and product teams in maximising performance, scalability, and reliability across the infrastructure stack.
• Establish and maintain model and data lifecycle capabilities, including dataset versioning, lineage tracking, reproducibility standards, and integration with model registries.
• Lead the evolution of knowledge graph infrastructure, driving technology selection, migration strategies, performance optimisation, and integration with AI workflows.
• Serve as the primary technical coordination point across AI Research, Data Engineering, MLOps, Product, and Infrastructure teams, resolving dependencies and prioritising activities critical to delivery.
• Provide technical leadership for vendor selection, procurement, and technology partnerships, advising on compute architectures, GPU specifications, AI platforms, and integration approaches.
• Define platform engineering standards, governance, and best practices while mentoring engineers and promoting operational excellence across AI and platform teams.

Skills & Experience

• Significant experience designing, building, and operating large-scale AI, machine learning, or distributed computing platforms in enterprise environments.
• Deep understanding of LLM architectures and their interaction with GPU infrastructure, including CUDA, cuDNN, NCCL, kernel-level acceleration libraries, and distributed training frameworks such as PyTorch.
• Strong knowledge of distributed training and inference strategies, including tensor, pipeline, data, and expert parallelism approaches.
• Experience optimising LLM inference workloads using technologies such as vLLM, TensorRT-LLM, NVIDIA NIM, SGLang, or similar high-performance serving frameworks.
• Expertise in model optimisation techniques including quantisation, mixed precision training and inference (FP8, GPTQ, AWQ, LoRA), and performance tuning for large-scale model deployment.
• Strong background in AWS cloud services, high-performance computing, distributed systems, containerised environments, and infrastructure automation.

IQVIA is a strong advocate of diversity and inclusion in the workplace. We believe that a work environment that embraces diversity will give us a competitive advantage in the global marketplace and enhance our success. We believe that an inclusive and respectful workplace culture fosters a sense of belonging among our employees, builds a stronger team, and allows individual employees the opportunity to maximize their personal potential.

IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide.

Location: London, United Kingdom
Type: Full time