Advisory AI Infrastructure / MLOps Engineer

Description and Requirements

This role is open for the Edinburgh, Scotland location only. Candidates must be based there, as the position requires working from the office at least three days per week (3:2 hybrid policy).

The Lenovo AI Technology Center (LATC)—Lenovo’s global AI Center of Excellence—is driving our transformation into an AI-first organization. We are assembling a world-class team of researchers, engineers, and innovators to position Lenovo and its customers at the forefront of the generational shift toward AI. Lenovo is one of the world’s leading computing companies, delivering products across the entire technology spectrum, spanning wearables, smartphones (Motorola), laptops (ThinkPad, Yoga), PCs, workstations, servers, and services/solutions. This unmatched breadth gives us a unique canvas for AI innovation, including the ability to rapidly deploy cutting-edge foundation models and to enable flexible, hybrid-cloud, and agentic computing across our full product portfolio. To this end, we are building the next wave of AI core technologies and platforms that leverage and evolve with the fast-moving AI ecosystem, including novel model and agentic orchestration & collaboration across mobile, edge, and cloud resources. This space is evolving fast and so are we. If you’re ready to shape AI at a truly global scale, with products that touch every corner of life and work, there’s no better time to join us.

Lenovo is seeking a highly skilled AI Infrastructure Engineer / AI Operations Engineer to join our growing team. This critical role will focus on designing, building, and maintaining the infrastructure and tools necessary for efficient AI model development, deployment, and operation. Your expertise will enable our data scientists and engineers to focus on high-priority tasks while ensuring seamless operation of AI models in production. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision!

Responsibilities:

AI Infrastructure Design and Implementation: Design, build, and maintain scalable and efficient AI infrastructure, including compute resources, storage solutions, and networking configurations. AI Model Deployment and Management: Develop and implement processes for deploying, monitoring, and managing AI models in production environments. Automation and Tooling: Create and maintain automation scripts and tools for AI model training, testing, evaluation, and deployment in a continuous integration / continuous delivery (CI/CD) pipeline. Collaboration and Support: Work closely with data scientists, engineers, and other stakeholders to ensure smooth operation of AI systems and provide support as needed. Performance Optimization: Continuously monitor and optimize AI infrastructure and models for performance, scalability, utilization, and reliability. Security and Compliance: Ensure AI infrastructure and models comply with relevant security and regulatory requirements.

Qualifications:

Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field. 8+ years of experience in software engineering, DevOps, or a related field. Strong background in computer systems, distributed systems, and cloud computing. Proficient in Linux system administration, including package management, user/group management, file system navigation, shell scripting Bash), and system configuration systemd, networking). Proficiency in programming languages such as Python, Java, or C++. Experience with AI-specific infrastructure and tools NVIDIA GPUs and CUDA). Experience with managing high-performance computing (HPC) clusters, including job scheduling, resource allocation, and cluster maintenance. Experience with setting up multi-node distributed GPU clusters, leveraging Slurm, Kubernetes or related software stacks.Familiarity configuring job scheduling tools Slurm). Experience with AI infrastructure, model deployment, and management. Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Ability to work in a fast-paced, dynamic environment.

Bonus Points:

Familiarity with AI and machine learning frameworks PyTorch). Familiarity with cloud platforms AWS, GCP, Azure). Experience with containerization Docker) and orchestration Kubernetes). Experience with monitoring and logging tools Prometheus, Grafana).

What we offer:

Opportunities for career advancement and personal development Access to a diverse range of training programs Performance-based rewards that celebrate your achievements Flexibility with a hybrid work model (3:2) that blends home and office life Electric car salary sacrifice scheme  Life insurance

 #LATC