Kerja Sepenuh Masa, Full Stack AI Solution Engineer di Lenovo Federal Territory - Maukerja

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).

Undertake the research and engineering implementation of enterprise-level RAG systems, build hybrid retrieval architecture integrating keyword, vector and graph retrieval, realize adaptive document chunking and reranking model fine-tuning, land full-link hallucination suppression strategies, and continuously improve knowledge retrieval accuracy and LLM answer robustness.

Design and build complex business Agent orchestration platforms, implement multi-tool chained calling, hierarchical memory management, dynamic prompt routing and automatic Agent failure retry mechanisms, and standardize human-machine collaborative workflow for complex industrial and business scenarios.

Build standardized prompt engineering systems, complete NL2SQL solution design and tuning, optimize conversational data analysis interaction logic and user intent recognition capabilities, establish prompt version management, quantitative effect evaluation and multi-turn dialogue intent clarification mechanisms, and improve the practicability and stability of LLM dialogue services.

Build end-to-end observability systems for LLM applications, realize prompt parameter tracking, retrieval log collection and monitoring, model output hallucination detection, and automatic user Q&A effect scoring, support closed-loop quantitative iteration and continuous optimization of AI applications.

Encapsulate reusable AI application basic components including general knowledge base access modules, Agent tool plugin market and unified multi-model invocation gateway. Build A/B testing systems to support gray-scale comparison and iterative optimization of RAG retrieval strategies, prompt templates and Agent business logic.

Optimize operating costs of large-scale AI applications via LLM caching, quantitative inference, vector storage tiered management and idle computing power recycling scheduling. Build AI security and compliance architecture covering prompt injection prevention, knowledge base data desensitization, model access authentication and user dialogue data standardized compliance management.

Design cross-terminal unified AI service gateway, unify logging, authentication and rate limiting standards for RAG, Agent and LLM fine-tuning businesses. Carry out AI technical debt governance, complete standardized transformation of scattered stock RAG/Agent applications, and unify AI project development, deployment and operation specifications.

Promote the upgrade of POC-level AI solutions to enterprise-scale architectures, implement vector database sharding and incremental knowledge synchronization, optimize massive document retrieval performance. Build high-availability LLM inference architecture with full-link fault tolerance capabilities including circuit breaking, rate limiting, caching and dynamic scaling.

Sort out structured workflows for complex industrial Agents, convert technical architectures and indicators into quantifiable business value and high-level reporting metrics. Conduct cross-team AI architecture interpretation for product and business teams, and precipitate standardized AI POC delivery templates including cost estimation, performance baselines, risk lists and large-scale transformation plans.

1. Solid practical experience in end-to-end enterprise-level LLM, RAG and Agent project landing, capable of independent architecture design, performance tuning and online iteration of large-scale AI applications.

2. In-depth understanding of systematic prompt engineering and NL2SQL technology, proficient in multi-turn dialogue context management and intent clarification, with practical experience in prompt version management and effect quantitative evaluation.

3. Familiar with vector database engineering, hybrid retrieval optimization and reranking tuning, able to solve core problems such as insufficient retrieval accuracy, model hallucination and massive data retrieval performance bottlenecks.

4. Proficient in Databricks platform application and MLOps system construction, familiar with data lake + vector database fusion architecture and Delta Lake data governance tuning.

5. Have in-depth practice in AI application cost optimization, security compliance architecture construction and technical debt governance, with enterprise-level high availability, standardization and compliance awareness.

6. Possess excellent business thinking and cross-team communication capabilities, able to translate technical advantages into business value and promote standardized and large-scale landing of AI projects.