Full Stack AI Solution Engineer

Lenovo; Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

Lenovo in Malaysia seeks a Senior AI Architect to lead end-to-end enterprise AI solutions, including RAG systems, multi-tool orchestration, and scalable LLM platforms.

You will design and implement retrieval architectures, prompt strategies, observability, and deployment patterns across large-scale AI applications, collaborating with cross-functional teams.

Qualifications

• Solid practical experience in end-to-end enterprise-level LLM, RAG and Agent project landing.
• In-depth understanding of systematic prompt engineering and NL2SQL technology.
• Familiar with vector database engineering, hybrid retrieval optimization and reranking tuning.
• Proficient in Databricks platform and MLOps system construction, data lake + vector DB fusion.
• Experience in AI cost optimization, security compliance architecture and governance.
• Strong business thinking and cross-team communication; translate tech to business value.

Responsibilities

• Undertake research and engineering of enterprise-level RAG systems and hybrid retrieval.
• Design complex business Agent orchestration platforms and multi-tool calls.
• Develop systematic prompt engineering, NL2SQL and dialogue optimization.
• Build LLM observability with logging, prompts tracking and hallucination detection.
• Encapsulate reusable AI components and unified AI gateway for delivery.
• Support cost optimization, security compliance, and governance across AI projects.

Skills

RAG systems

Agent orchestration

Prompt engineering

LLM architectures

Databricks

MLOps

Vector databases

Job description

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 www.lenovo.com , and read about the latest news via our StoryHub .

Description and Requirements

Key Responsibilities

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.

2. Complex Business Agent Orchestration

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.

3. Systematic Prompt Engineering & Dialogue Optimization

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.

4. LLM Full-link Observability Construction

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.

6. AI Cost Optimization & Security Compliance Architecture

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.

7. Unified AI Gateway & Technical Debt Governance

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.

8. AI Architecture Upgrade from POC to Enterprise Scale

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.

9. Business Value Transformation & Standardized Delivery

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.

Job Requirements

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.

If you require an accommodation to complete this application, please contact [e-mail]