Senior AI Platform Engineer

• 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
• 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

Benefits

• Medical, dental and vision coverage
• Telemedicine
• Critical illness coverage
• Prescription coverage
• Disability, accidental death, pet and life insurance
• Employee Assistance Programs
• Mental health support
• Well-being programs and events
• Flexible work environment
• Time off for leisure, personal time and corporate holidays
• Parental leave for birth or adoption
• Paid leave for other life matters (i.e., illness, bereavement, jury duty, military service, etc.)
• Training systems
• AI talent marketplace tool
• Leadership training programs
• Employee recognition programs
• Mentorship programs
• Service awards
• Tuition reimbursement
• Paid volunteer day
• Donation matching
• Employee resource groups
• Office events
• Environmental commitments
• UN Global Impact member
• Retirement and savings plans
• Identity theft protection
• Commuter benefits

• 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
• Proven ability to lead complex cross-functional initiatives, influence technical direction, and communicate effectively with engineering, research, product, and executive stakeholders
• Experience optimising LLM inference workloads using technologies such as vLLM, TensorRT-LLM, NVIDIA NIM, SGLang, or similar high-performance serving frameworks
• Advanced experience profiling, troubleshooting, and optimising GPU workloads using tools such as NVIDIA Nsight, DCGM, and related ecosystem technologies
• Strong background in AWS cloud services, high-performance computing, distributed systems, containerised environments, and infrastructure automation
• Strong knowledge of distributed training and inference strategies, including tensor, pipeline, data, and expert parallelism approaches
• Expertise in model optimisation techniques including quantisation, mixed precision training and inference (FP8, GPTQ, AWQ, LoRA), and performance tuning for large-scale model deployment
• Demonstrated success bridging research and production environments, enabling rapid experimentation while maintaining operational excellence, governance, security, and reliability
• Experience with workload orchestration technologies such as Slurm, Kubernetes, Ray, or equivalent distributed compute frameworks