Senior AI Engineer

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 #196 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 . About the Role We are looking for a Senior AI Engineer to design, build, and ship AI-powered capabilities for our enterprise platforms — including AI agents, copilots, and intelligent automation embedded in real business workflows (e.g., billing operations, service management, customer onboarding). This is a builder role, not a research role. We expect you to be fluent with modern AI-assisted development tools, but that alone is not enough: you must have shipped AI agents or AI applications to real users, and understand what it takes to make LLM-based systems reliable, safe, and maintainable in production. Key Responsibilities AI Application & Agent Development Design and implement AI agents and LLM-powered applications: task decomposition, tool/function calling, multi-step orchestration, and human-in-the-loop workflows Build RAG pipelines and knowledge systems: document ingestion, chunking, embedding, retrieval strategy, and grounding quality Integrate LLM capabilities with enterprise systems via APIs, event-driven architecture, and middleware; handle auth, rate limits, and failure modes Design prompt/context architectures that are versioned, testable, and maintainable — not one-off prompt hacking Production Engineering & Quality Build evaluation frameworks for AI features: golden datasets, automated eval pipelines, regression testing for prompt/model changes Implement guardrails and safety controls: input/output validation, hallucination mitigation, PII handling, and audit logging Own observability for AI systems: tracing, token/cost monitoring, latency optimization, and model fallback strategies Make pragmatic model and architecture choices (hosted APIs vs. self-hosted, model selection, caching, fine-tuning vs. prompting) based on cost, latency, and quality trade-offs Collaboration & Enablement Partner with product analysts and business stakeholders to turn ambiguous AI use cases into scoped, buildable solutions Establish engineering best practices for AI-assisted development (Claude Code, Cursor, Copilot, etc.) across the team Mentor engineers on agent design patterns, evaluation discipline, and responsible AI practices Required Qualifications Bachelor's degree or above in Computer Science, Software Engineering, or related field 5+ years of software engineering experience, with 2+ years building LLM-based applications or AI agents At least one AI agent or AI application shipped to production with real users — you can walk us through the architecture, the failure modes you hit, and how you addressed them Hands-on depth in the modern AI stack: LLM APIs (Anthropic, OpenAI, or equivalent) including tool use / function calling and structured outputs Agent frameworks or hand-rolled orchestration (e.g., LangGraph, MCP-based tooling, or custom-built agent loops) — and clear opinions on when a framework is the wrong choice RAG and vector search (embedding models, vector databases, retrieval evaluation) Strong general engineering fundamentals: Python and/or TypeScript, API design, testing, CI/CD, and version control — AI tools accelerate you, but your code must stand on its own without them Experience with evaluation and observability for non-deterministic systems: you can explain how you measured whether an AI feature actually worked Able to communicate technical trade-offs clearly to non-engineering stakeholders in English [Optional if bilingual environment] Fluent in Mandarin and English Preferred Qualifications Experience embedding AI features into enterprise systems (ERP, billing, ITSM/ServiceNow, CRM) rather than standalone consumer apps Familiarity with Model Context Protocol (MCP) or building tool integrations for agents Experience with fine-tuning, model distillation, or self-hosted open-weight models (vLLM, etc.) Knowledge of enterprise AI governance: data privacy, compliance constraints, model risk management Cloud platform experience (AWS Bedrock, Azure OpenAI, GCP Vertex AI) Contributions to open-source AI projects, or a public portfolio of shipped AI work