Principal Engineer, AI Architecture

We are Lenovo. We do what we say. We own what we do. We WOW our customers.

Lenovo is a US$57 billion revenue global technology powerhouse, ranked #248 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).

Summary:

Lenovo is seeking a highly experienced and visionary Principal AI Architect to lead the design and implementation of our next-generation AI systems. This is a pivotal role responsible for the overall technical direction, architecture, and scalability of our AI platform. The ideal candidate is a deep generalist with a comprehensive understanding of the entire AI stack - from foundational models to application-level agents - and the ability to articulate complex technical concepts to both technical and non-technical audiences. You will be one of the go-to experts for all things AI, guiding engineering teams and driving innovation in a rapidly evolving field. If you are passionate about making Smarter Technology For All, come help us realize our Hybrid AI vision!

Responsibilities:

• System Architecture: Design and maintain the overall architecture of our AI systems, ensuring scalability, reliability, and performance.
• Model Expertise: Possess in-depth knowledge of a wide range of AI models, including Large Language Models (LLMs), multimodal models (vision, speech, text), and their underlying principles.
• Training & Fine-tuning: Architect and oversee the training and fine-tuning of AI models, including techniques like Supervised Fine-Tuning (SFT), LoRA (Low-Rank Adaptation), and prompt tuning.
• Agent Design: Lead the design and implementation of intelligent agents, encompassing components like intent understanding, task decomposition and planning, and tool calling (e.g., using MCP - Model Context Protocol).
• Knowledge Management: Design and implement robust knowledge management systems, including vector databases, reranking models and algorithms, and knowledge graphs.
• Output Optimization: Select output sampling strategies to ensure high-quality, relevant, and coherent responses.
• Technical Leadership: Provide technical leadership and mentorship to engineering teams, guiding them through the implementation of complex AI solutions.
• Research & Innovation: Stay abreast of the latest advancements in AI research and identify opportunities to apply them to our products and services.
• Cross-Functional Collaboration: Work closely with product management, data science, and engineering teams to define requirements and deliver innovative AI solutions.
• System Documentation: Create and maintain comprehensive documentation of the AI system architecture and components.

Required Qualifications:

• 15+ years of experience designing and implementing AI systems.
• Understanding of Machine Learning and Deep Learning principles.
• Extensive experience with Large Language Models (LLMs) and Transformer architectures.
• Hands-on experience with multimodal models.
• Proficiency in programming languages such as Python.
• Experience with Machine Learning frameworks such as PyTorch.
• Strong understanding of vector databases and embedding models.
• Experience with knowledge graphs and knowledge base management.
• Mastery with prompt engineering techniques and prompt optimization.

Preferred Qualifications:

• Excellent communication, interpersonal, and presentation skills, including clear descriptions and document generation, such as class diagrams, sequence diagrams, and protocol definitions.
• Experience with MLOps practices and deploying AI models to production.
• Contributions to open-source AI projects.
• Publications in leading AI conferences or journals.
• Familiarity with Atlassian tools (Jira, Confluence)