AI Infrastructure Architecture

We welcome applications from professionals at all career levels, from Analyst through to Senior Manager.

We are looking for AI Infrastructure Architects at multiple experience levels to design, build, deploy and optimise the infrastructure that powers real-world artificial intelligence and machine learning solutions. This is a hands-on role spanning cloud, on-premises and hybrid environments, with work across GPU clusters, distributed training, model serving, data pipelines, CI/CD, InfraOps, MLOps, monitoring and enterprise integration.

Whether you are developing your infrastructure engineering skills, leading delivery workstreams, owning complex architecture decisions or setting technical strategy at the practice level, you will help create scalable, secure, cost-efficient AI/ML infrastructure that delivers measurable business value for clients.

What you will

• Design, build, configure and optimise AI/ML infrastructure across public cloud, on-premise and hybrid environments.
• Provision and tune compute resources, including GPU clusters, distributed training environments, networking, storage, orchestration and model-serving platforms.
• Write, review and maintain code, scripts, infrastructure-as-code, deployment automation and CI/CD pipelines for reliable AI system releases.
• Deploy AI systems, machine learning models and data pipelines into production, ensuring performance, reliability, security and compliance.
• Monitor infrastructure and AI systems across InfraOps and MLOps, including observability, alerting, model performance, drift, cost and resource utilisation.
• Troubleshoot and resolve issues across the full computational stack, from hardware and networking through software, pipelines and models.
• Evaluate tools, frameworks, platforms and emerging technologies, advising on where they fit within scalable AI infrastructure solutions.
• Collaborate with clients, stakeholders, engineers and cross-functional teams to translate business requirements into practical, defensible architecture decisions.
• Document standards, procedures, design decisions and best practices to support repeatable delivery and knowledge sharing.

• A degree in Computer Science, Computer Engineering or a related engineering field, or equivalent practical experience.
• Strong understanding of AI, machine learning and the infrastructure required to train, deploy, monitor and operate AI/ML systems.
• Hands-on experience coding, building, monitoring and troubleshooting AI/ML applications or infrastructure.
• Programming capability in languages such as Python, Java or C++, plus scripting and automation skills.
• Experience with cloud or on-premises infrastructure, ideally including hyperscaler platforms such as AWS, Azure or Google Cloud.
• Knowledge of containers, orchestration, model deployment frameworks, CI/CD, infrastructure-as-code, monitoring and production operations.
• Experience with data pipelines and workflow management tools such as Apache Airflow or Kubeflow.
• Strong problem-solving, communication and collaboration skills, with the ability to work effectively in fast-paced, cross-functional environments., * Proven ability to lead AI/ML infrastructure projects, teams or architecture workstreams from concept through delivery.
• Experience making and documenting architecture decisions across compute, networking, storage, orchestration, model serving, security, compliance, cost and scalability.
• Deep expertise in at least one hyperscale platform, with broader awareness of AI/ML services, accelerators, interconnects, performance levers and cost optimisation options.
• Ability to advise clients and senior stakeholders, translating complex technical trade-offs into clear recommendations linked to business outcomes.
• Experience defining architecture standards, reference patterns, roadmaps, governance, observability strategies and best practices for enterprise-scale AI/ML infrastructure.