AI Solution Architect

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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.

- Define measurable service-level objectives and performance budgets for latency, throughput, concurrency, model response, device connectivity, reliability, and cost.

- Lead capacity planning, load and stress testing, bottleneck analysis, caching, asynchronous processing, and model-inference optimization.

- Design observability using logs, metrics, traces, AI quality signals, usage analytics, and cost telemetry to support rapid diagnosis and continuous improvement.

- Apply FinOps principles to optimize model, compute, storage, networking, and data-processing costs without compromising required outcomes.

Security, Responsible AI & Operational Excellence

- Embed security and privacy into solution designs, including identity, authorization, secrets, encryption, API protection, tenant isolation, data governance, and supply-chain controls.

- Apply responsible AI practices covering safety, transparency, groundedness, bias assessment, content controls, human oversight, and auditability.

- Define CI/CD, MLOps/LLMOps, automated evaluation, release, rollback, model-update, and incident-response approaches.

- Conduct architecture and production-readiness reviews and ensure that key technical decisions are documented.

Job Requirements

- Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field; a Master’s degree is a plus.

- 8+ years of experience in software engineering, solution architecture, cloud engineering, or related technical roles, including significant experience delivering cloud-based applications.

- Demonstrated experience designing and implementing production AI/ML or generative AI applications from concept through deployment and operations.

- Strong competency in modern AI full-stack development, including application engineering, model integration, retrieval and agentic patterns, evaluation, deployment, and observability.

- Deep knowledge of cloud architecture, distributed systems, APIs, microservices, containers, Kubernetes, event-driven design, data stores, caching, and asynchronous processing.

- Hands-on proficiency in Python and at least one additional modern application language such as TypeScript/JavaScript, Java, C#, or Go.

- Experience with modern AI frameworks and services, such as PyTorch, TensorFlow, Hugging Face, ONNX Runtime, LangChain, LlamaIndex, Semantic Kernel, or comparable technologies.

- Experience with at least one major public cloud platform and its AI, data, networking, security, monitoring, and deployment services.

- Proven ability to analyze and improve application and infrastructure performance at scale.

- Strong written and verbal communication, architecture documentation, stakeholder management, and technical leadership skills.

- Ability to work effectively with globally distributed, cross-functional teams and to lead through influence.

Preferred Qualifications

- Experience building hybrid AI solutions spanning cloud services, PCs, mobile devices, edge systems, or embedded clients.

- Experience with device-cloud communication, telemetry, remote configuration, real-time messaging, and secure client agents.

- Familiarity with enterprise SaaS platforms, multi-tenant architecture, identity platforms, policy enforcement, and data residency requirements.

- Experience with RAG, vector search, knowledge graphs, AI agents, multimodal models, small language models, model optimization, or hardware-accelerated inference.

- Experience establishing reusable architecture patterns, internal developer platforms, SDKs, reference implementations, or centers of excellence.

- Understanding of AI application evaluation, red teaming, threat modeling, privacy engineering, and responsible AI governance.

- Experience working directly with product segments, customers, partners, or field teams to move solutions from discovery to measurable adoption.

Core Competencies

- Solution thinking: connects business outcomes to practical, reusable technical architectures.

- Hands-on leadership: prototypes critical paths, validates assumptions, and guides teams through difficult implementation decisions.

- Architecture judgment: makes clear trade-offs across experience, quality, security, performance, cost, and delivery speed.

- Collaboration: builds alignment across segment, product, design, data, cloud, device, security, and engineering teams.

- Execution focus: moves concepts into production with measurable acceptance criteria and operational ownership.

- Learning agility: continuously evaluates emerging AI capabilities and applies them where they create meaningful value.

Measures of Success

- AI applications progress efficiently from discovery and blueprint to production deployment and measurable adoption.

- Solutions reuse UDS, UDC, and other CSW assets where appropriate, reducing duplication and improving delivery consistency.

- Architectures meet agreed targets for quality, security, reliability, latency, scalability, operability, and cost.

- Reference patterns and platform improvements accelerate subsequent segment use cases.

- Stakeholders have clear technical decisions, risks, dependencies, and implementation roadmaps.