Senior AI Engineer
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 [external link] and read about the latest news via our StoryHub.
Description and Requirements
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., 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)
- United States of America - North Carolina - Morrisville
- United States of America
- United States of America - North Carolina
- United States of America - North Carolina - Morrisville
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Apply
If you require an accommodation to complete this application, please contact
ability@lenovo.com
Skills mentioned
- Agentic AI
- AI
- Anthropic
- API
- API Design
- Automation
- AWS Bedrock
- Azure
- CI Cd
- Claude Code
- Cloud
- CRM
- Customer Onboarding
- Data Privacy Law
- Design Patterns
- ERP
- Event Driven Architecture
- Fine Tuning
- GCP
- Langgraph
- LLM
- Llmops
- Mcp
- Observability
- Openai
- Python
- Rag
- Risk Management
- Servicenow
- TypeScript
- Vector Databases
- Vector Search
- Version Control
- Vertex AI
- Vllm
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