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# Senior AI Engineer

- **Company:** Lenovo
- **Location:** Morrisville, NC, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Cloud Computing, Encodings, Continuous Integration, Cursor, Programming Tools, Middleware, Python (Programming Language), Knowledge-Based Systems, Open Source Technology, Regression Testing, OpenAI, Microsoft Copilot, Search Technologies, Software Engineering, TypeScript, Design of Telemetry/logging Agents, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Claude Code, Google Vertex AI, Model Validation, Caching, Generative AI, Agentic-AI, Event Driven Architecture, Information Technology, Virtual Agents, Invoking Functions, Api Design, Model Context Protocol, Software Version Control, Human in the Loop, Servicenow
- **Published:** October 7, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/43077986/1

## About the Role

* 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, * 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)
 * 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

 * 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

## Description

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!, 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