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Lenovo Hiring! Full Time Senior AI Engineer in Federal Territory - Ricebowl

Senior AI Engineer

Undisclosed

KL City, Federal Territory

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Working Location

  • Kuala Lumpur Federal Territory Malaysia

Job Description

Responsibilities

General Information

Req #
WD00102206
Career area:
Hardware Engineering
Country/Region:
Malaysia
State:
Wilayah Persekutuan Kuala Lumpur
City:
Kuala Lumpur
Date:
Tuesday, July 14, 2026
Working time:
Full-time
Additional Locations:
  • Malaysia

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 #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 *************, 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.

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
Additional Locations:
  • Malaysia
  • Malaysia

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