AI Solution Architect
Company: Lenovo
Role summary
AI Solution Architect is a full-time role at Lenovo in Beijing, China (on-site). Posted 2026-10-29. Experience level: 10+.
- Job title
- AI Solution Architect
- Company
- Lenovo
- Location
- Beijing, China
- Work arrangement
- on-site
- Employment type
- full-time
- Category
- Software Engineering
- Experience level
- 10+
- Date posted
- 2026-10-29
You’ll be joining a global tech powerhouse to lead the integration of advanced AI technology across a massive, full-stack product portfolio. It’s an incredible opportunity for a seasoned expert to shape how millions of customers interact with next-generation, AI-optimized hardware and systems.
Job description
Why Work at Lenovo
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 .
Description and Requirements
Job Description
At Lenovo, we Never Stand Still. Every day, every employee is focused on moving forward, challenging traditional limits, and always seeking a better way. We are looking for an experienced AI Solution Architect to design and implement differentiated AI applications that solve meaningful customer and business problems.
This role sits at the intersection of solution architecture, AI engineering, cloud platforms, and customer delivery. The architect will work closely with segment, product, engineering, security, and business teams to discover opportunities, define solution blueprints, create prototypes, and guide applications into secure, scalable production deployments.
The ideal candidate is highly competent in modern AI full-stack development (AIFSD), current AI technology stacks, cloud-native architecture, and performance engineering. The individual will reuse and extend CSW assets, including the UDS cloud platform and UDC client agents, to create integrated cloud-to-device AI solutions rather than isolated proofs of concept.
Role Overview
The AI Solution Architect will own the end-to-end technical solution for AI applications, from problem framing and architecture through implementation, validation, deployment, and operational optimization. The role requires hands-on technical depth, strong architecture judgment, and the ability to align diverse stakeholders around a practical delivery plan.
Success in this role means converting business outcomes into reusable architectures, accelerating delivery through existing CSW platform capabilities, and ensuring that AI applications meet enterprise expectations for security, reliability, performance, observability, cost, and responsible AI.
Job Responsibilities
AI Solution Architecture & Blueprinting
Design end-to-end architectures for generative AI, agentic AI, retrieval-augmented generation, predictive AI/ML, and hybrid cloud-edge applications.
Create solution blueprints that define application components, model and data flows, APIs, integration patterns, deployment topology, security controls, non-functional requirements, and operational ownership.
Evaluate build, buy, reuse, and extend options; select appropriate models, frameworks, vector stores, orchestration patterns, and cloud services based on measurable requirements.
Define reusable reference architectures, design patterns, guardrails, and technical standards for AI application teams.
AI Application Design & Implementation
Lead and contribute hands-on to prototypes, proofs of value, minimum viable products, and production-grade AI applications.
Apply modern AIFSD practices across user experience, APIs, agent and workflow orchestration, model integration, retrieval pipelines, data services, evaluation, deployment, and monitoring.
Engineer AI applications using current technology stacks such as Python, TypeScript/JavaScript, REST/gRPC APIs, containers, Kubernetes, model gateways, vector databases, and MLOps/LLMOps tooling.
Design prompt, retrieval, tool-use, memory, evaluation, and human-in-the-loop patterns that improve quality, traceability, and control.
Guide teams in model selection, adaptation, grounding, testing, deployment, versioning, and lifecycle management.
Segment & Cross-Functional Delivery
Partner with segment and business teams to understand user journeys, prioritize high-value opportunities, and convert business needs into implementable AI solutions.
Facilitate architecture workshops, technical discovery sessions, design reviews, and trade-off decisions with product, UX, data, security, cloud, device, and engineering stakeholders.
Communicate architecture and delivery choices clearly to both executive and engineering audiences, including assumptions, risks, dependencies, cost, and expected outcomes.
Support engineering teams through implementation, integration, troubleshooting, performance testing, and production readiness.
CSW Platform Reuse & Integration
Use and extend CSW assets, including the UDS cloud platform and UDC client agents, as foundational capabilities for AI solutions.
Design secure cloud-to-device integrations for identity, telemetry, configuration, orchestration, notifications, data exchange, and AI-enabled workflows.
Identify platform gaps and collaborate with UDS and UDC teams to define reusable capabilities that benefit multiple segments and applications.
Ensure solutions are modular and portable, with clear interfaces that reduce duplication and accelerate adoption across product teams.
Cloud Architecture, Scale & Performance
Architect resilient, multi-tenant, cloud-native services with appropriate scalability, availability, disaster recovery, data protection, and regional deployment considerations.
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