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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Advisory AI Infrastructure Engineer - **Company:** Lenovo - **Location:** Edinburgh, UK - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), A/B Testing, Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Bash Shell, C++ (Programming Language), Cloud Computing, Computer Clusters, Nvidia CUDA, Computer Engineering, System Configuration, Continuous Integration, DevOps, File Systems, Distributed Systems, Monitoring of Systems, InfiniBand, Job Scheduling, Python (Programming Language), Linux System Administration, Machine Learning, Node.Js, Open Source Technology, Package Management Systems, Performance Tuning, Ansible, Prometheus, Shell Script, Software Engineering, AI Infrastructure, Rust (Programming Language), Datadog, Pulumi, Graphics Processing Unit (GPU), Google Cloud, Pytorch, Large Language Models, Grafana, Kubernetes Helm Charts, HybridCloud, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Slurm, Machine Learning Operations, Terraform, Docker - **Published:** June 24, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=17e1b83fc5625cd1 ## About the Role Do you have a valid Dangerous Goods Driver's License license?, Do you have experience in Terraform?, Do you have a Master's degree?, * Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field. Advanced degree preferred. * 8+ years of experience in software engineering, infrastructure engineering, DevOps/SRE, or a related field, with at least 4 years focused on AI/ML infrastructure. * Demonstrated experience leading or mentoring engineering teams on infrastructure projects. * Deep expertise in computer systems, distributed systems, and cloud computing architectures. * Extensive experience designing, deploying, and managing multi-node distributed GPU clusters using Slurm, Kubernetes, or equivalent orchestration platforms. * Expert-level Linux system administration, including package management, user/group management, file system internals, shell scripting (e.g., Bash), networking, and system configuration (e.g., systemd, kernel tuning). * Strong proficiency in Python and at least one additional systems language (e.g., Go, C++, Rust, Java). * Deep experience with AI-specific hardware and software stacks (e.g., NVIDIA GPUs, CUDA, cuDNN, NCCL, InfiniBand/RoCE networking). * Proven track record managing high-performance computing (HPC) environments, including job scheduling, resource allocation, cluster maintenance, and performance tuning. * Significant experience with AI model deployment, serving infrastructure, and lifecycle management in production. * Strong architectural thinking with the ability to balance trade-offs across performance, reliability, security, and cost. * Excellent communication and collaboration skills, with the ability to influence technical decisions across teams and present to senior leadership. * Ability to thrive in a fast-paced, ambiguous environment and drive clarity through technical leadership. Bonus Points * Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM). * Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and hybrid cloud architectures. * Advanced experience with containerization (Docker) and orchestration (Kubernetes), including custom operators, Helm charts, and GPU scheduling plugins. * Expertise with observability stacks (e.g., Prometheus, Grafana, ELK/OpenSearch, Datadog) for infrastructure and model monitoring. * Experience with infrastructure-as-code tools (e.g., Terraform, Ansible, Pulumi). * Contributions to open-source infrastructure or ML tooling projects. * Experience with large language model (LLM) training and inference infrastructure. ## Description Lenovo is seeking an accomplished Advisory AI Infrastructure Engineer to take a leadership role within our Advanced AI Technology Center. In this position, you will architect, build, and evolve the large-scale infrastructure and platforms that underpin AI model development, deployment, and operation across the organization. You will serve as a technical authority on infrastructure strategy, mentor junior engineers, drive best practices, and collaborate cross-functionally with research, engineering, and product teams. Your deep expertise will be instrumental in scaling our AI capabilities and ensuring production-grade reliability, performance, and security. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision! Responsibilities * AI Infrastructure Strategy and Architecture: Define and drive the long-term infrastructure strategy for AI workloads. Architect scalable, cost-efficient, and resilient compute, storage, and networking solutions that support the full AI lifecycle from experimentation through production. * Technical Leadership and Mentorship: Serve as a technical lead and mentor to infrastructure engineers, establishing engineering standards, conducting design reviews, and fostering a culture of operational excellence across the team. * AI Model Deployment and Platform Engineering: Design and own the platforms, frameworks, and processes for deploying, monitoring, and managing AI models at scale in production environments, including model serving, A/B testing infrastructure, and rollback mechanisms. * Advanced Automation and CI/CD: Architect and maintain sophisticated automation pipelines for AI model training, evaluation, testing, and deployment. Champion infrastructure-as-code practices and drive continuous improvement of CI/CD workflows. * Cross-Functional Collaboration: Partner with data scientists, ML engineers, product managers, and leadership to align infrastructure capabilities with research and business objectives. Act as the primary infrastructure point of contact for cross-team initiatives. * Performance Engineering: Lead efforts to profile, benchmark, and optimize AI infrastructure and model serving for throughput, latency, GPU utilization, scalability, and cost efficiency at scale. * Security, Compliance, and Governance: Establish and enforce security best practices, access controls, and compliance frameworks across AI infrastructure. Ensure adherence to relevant regulatory requirements and internal governance policies. * Capacity Planning and Cost Management: Own capacity forecasting, resource planning, and cost optimization for GPU clusters and associated infrastructure, balancing performance needs with budget constraints. ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Unleashing Potential Across Teams: The Power of Infrastructure as Code](https://www.wearedevelopers.com/videos/930-unleashing-potential-across-teams-the-power-of-infrastructure-as-code) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Terraform for Developers](https://www.wearedevelopers.com/videos/3-terraform-for-developers) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)