Senior AI Platform Engineer

About the role

IQVIA is looking for a Senior AI Platform Engineer to build and scale the infrastructure that lets its AI and machine learning teams develop, train, evaluate and deploy large language models (LLMs) and other advanced AI solutions. The role sits at the intersection of AI, cloud infrastructure, distributed systems and platform engineering, working with AI researchers, data engineers, MLOps specialists and product teams.

Key responsibilities

• Design, build and support the AI platform infrastructure behind large-scale machine learning and LLM initiatives.
• Work with AI research teams to turn their technical requirements into scalable infrastructure solutions.
• Develop and optimise cloud and on-premise compute environments, including GPU-enabled platforms.
• Improve the performance, reliability and scalability of model training and inference workloads.
• Help set best practices for model lifecycle management, reproducibility, dataset versioning and governance.
• Contribute to platform services that support AI experimentation and production deployment.
• Work with infrastructure, data engineering and MLOps teams to improve automation, monitoring and operational excellence.
• Evaluate and adopt new technologies and modern AI infrastructure tooling and engineering standards.

Required profile

• Experience building, operating or supporting large-scale AI, machine learning, cloud or distributed systems platforms.
• Strong understanding of cloud infrastructure, containerised environments and modern platform engineering practices.
• Hands-on experience with Python and modern software engineering practices.
• Ability to troubleshoot performance bottlenecks and improve complex workloads.
• Strong communication and collaboration skills across engineering and research teams.

Required skills

• Knowledge of AWS and infrastructure automation technologies.
• Experience with Kubernetes, Slurm, Ray or similar orchestration and distributed compute technologies.
• Nice to have: LLMs and generative AI platforms, GPU infrastructure, PyTorch, vLLM, TensorRT-LLM, NVIDIA NIM, knowledge graphs, HPC environments, and techniques such as quantisation, LoRA and mixed precision training.