Lenovo
Sr. AI Architect Engineer
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We are Lenovo. We do what we say. We own what we do. We WOW our customers.
Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).
This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.
Lenovo is seeking an experienced AI Architect specializing in hybrid cloud to shape how AI workloads are designed, placed, and scaled across on-premises data centers, 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! #LATC
Responsibilities
- Hybrid Cloud AI Architecture: Own the end-to-end reference architecture for AI workloads spanning the on-premises GPU estate, edge and device inference, and public cloud. Define the patterns teams use for 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 Modeling: 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 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.
- Platform and Vendor Evaluation: Lead the evaluation and selection of AI platform components, orchestration layers, serving frameworks, observability stacks, and cloud services. Run proofs of concept and produce recommendations grounded in measured results.
- 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.
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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 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 architecting GPU compute for AI, including cluster orchestration (Kubernetes, Slurm, or equivalent), scheduling and multi-tenancy, accelerator selection, and high-performance interconnect (e.g., NVIDIA GPUs, CUDA, NCCL, InfiniBand/RoCE).
- Solid command of the AI/ML lifecycle: data preparation and pipelines, distributed training and fine-tuning, inference optimization and model serving, MLOps, and production monitoring and evaluation.
- 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.
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Bonus Points
- Recognized architecture certification (AWS/Azure/Google Professional Cloud Architect, TOGAF, or equivalent).
- Experience with large language model training and inference architecture at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM).
- Experience with edge and on-device AI inference and the device-to-cloud continuum.
- Advanced Kubernetes experience: GPU scheduling plugins, multi-cluster and fleet management, service mesh, custom operators, and Helm.
- 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.
Benefits
- Holiday purchase
- Private medical
- Income protection
- Attractive pension scheme
- Positive work life balance
- Learning and development
- Life insurance
- Lenovo and Motorola products discounts
- Lifestyle discounts
- Cycle to work
- MyGymDiscounts
- Mortgage advice and support
- Referral bonus
- Free onsite parking
#LATC
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.
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