Senior AI Platform Engineer

Description

The company is expanding its AI capabilities to speed up innovation in healthcare and life sciences.

You will help build and scale the infrastructure that lets AI and machine learning teams develop, train, evaluate and deploy large language models (LLMs) and other advanced AI systems.

You will work where AI, cloud infrastructure, distributed systems and platform engineering meet, creating reliable, high-performance environments for research and product delivery.

You will work daily with AI researchers, data engineers, MLOps specialists and product teams to turn promising AI work into scalable, production-ready solutions with real impact in healthcare.

The company provides clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries.

Responsibilities

• Design, build and maintain the AI platform infrastructure behind large-scale machine learning and LLM work.
• Turn the technical needs of AI research teams into scalable infrastructure.
• Build and improve cloud and on-premise compute environments, including GPU-based platforms.
• Raise the performance, reliability and scalability of model training and inference workloads.
• Help set best practices for model lifecycle management, reproducibility, dataset versioning and governance.
• Build and evolve platform services that support AI experimentation and production deployment.
• Work with infrastructure, data engineering and MLOps teams to improve automation, monitoring and operations.
• Assess and adopt new technologies that improve platform capability and developer productivity.
• Help roll out modern AI infrastructure tools and engineering standards across the organisation.

Requirements

• Experience building, running or supporting large-scale AI, machine learning, cloud or distributed systems platforms.
• A solid grasp of cloud infrastructure, containerised environments and modern platform engineering.
• Experience with machine learning workloads and the infrastructure behind model training and deployment.
• Working knowledge of AWS and infrastructure automation tools.
• Experience with Kubernetes, Slurm, Ray or similar orchestration and distributed compute tools.
• Hands-on Python work and familiarity with modern software engineering practices.
• Ability to find performance bottlenecks and make complex workloads more efficient.
• Strong communication skills and the ability to work well across engineering and research teams.
• A drive to solve hard technical problems and build platforms that help others innovate.
• Particularly valued, one or more of the following:
• Experience with LLMs and generative AI platforms.
• Experience with GPU infrastructure and optimisation.
• Experience with PyTorch and distributed training frameworks.
• Experience with LLM inference technologies such as vLLM, TensorRT-LLM, NVIDIA NIM or similar.
• Experience with model optimisation techniques such as quantisation, LoRA, mixed precision training and inference optimisation.
• Experience with knowledge graphs and large-scale data platforms.
• Experience with high-performance computing (HPC) environments.

Conditions

Candidates must submit truthful, complete information; false statements or omissions can lead to disqualification or later dismissal, in line with applicable law.