Senior AI Engineer

• Req #: WD
• Career area: Hardware Engineering
• Country/Region: United States of America
• State: North Carolina
• City: Morrisville
• Date: Tuesday, October 6, 2026
• Working time: Full-time

• United States of America
  - North Carolina
  - Morrisville

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 , and read about the latest news via our Story Hub.

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.

This role will be hybrid in our Morrisville, NC office!

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

Basic 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

Preferred Qualifications

• Strong preference for fluency in Mandarin
• 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., Lang Graph, 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…