Consulting Engineer, Infrastructure & AI Architecture

Lenovo is seeking an experienced Architect specializing in infrastructure and software architecture to shape how AI workloads are designed, placed, and scaled across on-premises data centres, edge environments, and public cloud. 

This is an architecture leadership role: you will own the reference architectures, decision frameworks, and technical standards that guide how our teams build and run AI systems, and you will act as the trusted advisor who translates business and research objectives into implementable designs. Working across research, engineering, IT, security, and product, you will make and defend the trade-offs that determine performance, portability, compliance, and cost across the hybrid estate. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision! 

Acting as the owner of the design and governance of the platform, you will be directly responsible for the design, delivery, governance and the cost-effective use of the infrastructure. You will ensure that it meets the demands of varied consumers across Lenovo AI Technology Centre and wider across the CTO Organisation.

You will work in partnership with the Project Strategy Director and the AI Technical Director, and while not directly having line management responsibility you will coordinate with the other leaders to ensure correct prioritisation of technical platform progression alongside business value.

You will be responsible for ensuring best practice in software engineering is consistently adopted and adhered to across the UK sites and influence other wider sites.

You will be connected to the wider LATC architecture team and you will drive for alignment and ensure knowledge is signposted and shared continuously through a proactive communication approach.

Responsibilities

• Own the end-to-end reference infrastructure architecture for workloads spanning the on-premise server and public cloud compute and GPU estate. Define the patterns teams use for hosting, compute, data pipelines, training and fine-tuning, model serving, and retrieval-augmented systems.
• Workload Placement and Portability: Establish the decision framework that determines where a given workload runs, weighing data gravity, sovereignty, latency, accelerator availability, and cost. Design for portability so workloads can shift between environments without re-engineering.
• Architecture Governance and Standards: Chair design reviews, maintain architecture decision records, and define the golden patterns and guardrails that delivery teams build against. Serve as the technical approval authority for significant AI platform designs.
• Connectivity, Identity, and Data Architecture: Design the secure network topologies, federated identity and access models, and cross-environment data architectures that make a hybrid estate coherent, including dedicated interconnects, private endpoints, replication, and residency controls.
• Cloud Economics and TCO Modelling: Build and defend total-cost and unit-economics models comparing owned GPU capacity against cloud consumption, accounting for egress, commitments, and burst strategies. Inform buy-versus-build and capacity investment decisions with evidence.
• Technical Advisory and Enablement: Partner with software engineers, data scientists, ML engineers, platform engineers, product managers, and security to turn objectives into architecture and architecture into actionable roadmaps. Mentor engineers and fellow architects, and raise the design bar across the organization.
• Security, Compliance, and Resilience by Design: Embed access controls, regulatory requirements, and resilience and disaster-recovery patterns into architecture from the outset, working alongside security, legal, and compliance functions rather than retrofitting after the fact.

Qualifications

• Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field. Advanced degree preferred.
• 10+ years of experience in software, infrastructure, or platform engineering, including at least 3 years in a dedicated architecture or solution architecture role and 3 years working on Data, Cloud, AI/ML platforms or workloads.
• Demonstrated ownership of enterprise-scale hybrid or multi-cloud architectures spanning on-premises and public cloud environments, from design through adoption.
• Deep expertise in at least one major cloud platform (AWS, Azure, or GCP) with working knowledge of a second, alongside practical experience with on-premises and private cloud infrastructure.
• Strong grounding in hybrid networking: dedicated interconnects, transit and routing design, DNS, private service endpoints, and hybrid identity and federation models.
• Experience designing data architecture across environments, including storage tiering, replication and caching strategies, and the residency and sovereignty constraints that shape them.
• Sufficient hands-on fluency with infrastructure-as-code and automation (e.g., Terraform, Ansible, Kubernetes tooling) and working proficiency in Python to prototype, validate, and stress-test your own designs.
• Track record of building capacity and total-cost models that withstand scrutiny from engineering, finance, and executive stakeholders.
• Excellent written and verbal communication, with strong architecture documentation, decision records, and the presence to present and defend designs to senior leadership.
• Ability to influence without direct authority, build consensus across engineering, security, and business stakeholders, and drive clarity in a fast-paced, ambiguous environment.

Bonus Points

• Recognized architecture certification (AWS/Azure/Google Professional Cloud Architect, TOGAF, or equivalent)..
• Familiarity with private and hybrid cloud platforms (e.g., VMware, OpenShift, Nutanix, Azure Stack, AWS Outposts).
• Expertise with observability and FinOps tooling across hybrid estates (e.g., Prometheus, Grafana, ELK/OpenSearch, Datadog, cloud cost management platforms).
• Experience architecting under data sovereignty, regulated industry, or public sector constraints.
• Contributions to open-source infrastructure or ML tooling projects, or published architecture writing and conference talks.