AI Solution Architect

Lenovo seeks an AI Solution Architect to design and validate cost-effective AI solutions that meet client and business requirements. The role translates those requirements into implementable architectures, works closely with development and delivery teams through implementation, and advises sales and offering teams on technical matters rather than owning commercial decisions.

Responsibilities

• Design end-to-end architectures covering data pipelines, model serving, integration and security controls against functional and non-functional requirements. Define deployment strategies and a path from pilot to production, incorporating responsible AI, privacy and regulatory requirements.
• Support development through design reviews, backlog shaping and technical decisions. When useful, build mock-ups, prototypes, thin vertical slices, code spikes and reference implementations to test integration patterns, model behavior and performance assumptions.
• Contribute technical content to RFP/RFI responses, proposals and statements of work, including defensible effort and run-cost inputs. Design demos, proofs of concept and pilots with explicit success criteria, and turn repeatable client patterns into offerings, accelerators and reference architectures.

Qualifications and technical scope:

• A bachelor’s degree in computer science, engineering or a related field, or equivalent practical experience, is required, along with 2–5 years of combined solution architecture and software development experience and at least one design carried through to delivery.
• The technical scope includes Python, SQL, JavaScript or TypeScript; FastAPI, LangChain or LlamaIndex and an agent framework; LLM prompting and evaluation, RAG, embeddings, vector stores, fine-tuning, guardrails and inference cost and latency optimization. It also covers cloud AI/ML platforms, Docker, Kubernetes, Terraform, CI/CD, data pipelines and platforms, APIs, enterprise integration, security and non-functional architecture.
• Preferred qualifications include production AI/ML or generative-AI delivery, independent prototyping, enterprise application integration, MLOps/LLMOps, relevant professional cloud or AI certifications, and consulting or forward-deployed experience. Strong communication across engineers, clients and sales is valued; travel to client sites may be needed.

Success is measured by production delivery on scope and schedule, pilot-to-production conversion, reuse of architectures and accelerators, and feedback from development and sales teams.