AI Architect (內湖瑞光)

We are looking for an AI Architect to own the technical architecture of Advantech's enterprise AI platform, the shared "AI Factory" that is built once and reused across every business domain. The Architect owns the platform architecture, technical standards, and platform decisions that make AI agents and applications practical, secure, scalable, and cost-effective. This is a hands-on, senior individual-contributor role. You will set the architecture across models, agents, data, and integration; codify patterns and guardrails that engineers and domain teams reuse; and stay close enough to the code to prototype, review designs, and unblock delivery.

Responsibilities

Define and own the end-to-end platform architecture for enterprise AI agents and applications across the model, skill/MCP, data, and integration layers; Architect the NemoClaw model platform, including model evaluation and benchmarking, fine-tuning (SFT / LoRA / RLHF), model gateway and routing, harness engineering, and inference optimization; Design the skill/MCP platform, including enterprise skill registry, MCP server framework, and agent orchestration and A2A standards, with versioning and reuse governance; Establish the data platform architecture: semantic layer, master data, vector stores, knowledge bases, and RAG pipelines; Set standards for on-premises, data-isolated deployment, including RBAC and audit trails, prompt-injection defense, guardrail agents, and responsible-AI review gates; Design robust, secure integration patterns so agents can read and write real enterprise systems through governed APIs, data, and permissions; Define agent CI/CD, distributed tracing and telemetry, eval-driven regression testing, and token-cost and capacity practices; Codify architecture patterns, reusable implementation templates, and guardrails; raise the team's technical bar through design reviews and mentoring; Continuously scan, benchmark, and controlled-adopt frontier models, agent frameworks, RAG stacks, and emerging protocols; make build-vs-buy and platform-direction recommendations; Stay hands-on: prototype, review code and designs, and work side by side with AI engineers and the AI Project Manager; Ensure the platform scales across domains within clear performance, reliability, and cost targets; Provide architectural guidance to other enterprise application and system-integration projects when needed

Requirements

7–12 years of experience in software engineering, AI/ML or data engineering, or solution architecture, including 3+ years designing production AI/ML or large-scale distributed systems; A proven track record of designing and shipping production systems built on LLMs and agentic architectures (RAG, tool use, orchestration), not only prototypes or proofs of concept; Deep, hands-on expertise across the modern agent stack: models and inference, prompt and harness engineering, vector stores and retrieval, and APIs and system integration; Strong software-engineering fundamentals, with the ability to set and enforce architectural standards, run design and code reviews, and mentor engineers; Experience with cloud AI platforms (Azure and/or AWS) and with on-premises or data-isolated deployment; A solid grasp of AI and data governance, security controls, and human-in-the-loop design; Ability to translate business needs into architecture and to communicate technical direction clearly to both engineers and business stakeholders; Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience; Professional working proficiency in English and Mandarin Chinese

Nice to have

Experience building an internal AI platform (a model gateway, an MCP/skill registry, or an agent orchestration framework); Familiarity with fine-tuning techniques (SFT, LoRA, RLHF) and with model evaluation and benchmarking; Experience with observability and eval-driven testing for LLM/agent systems, and with token-cost and capacity management; Knowledge of enterprise systems and integration (CRM/SFDC, e-commerce, quotation, ERP) and API-gateway design; Exposure to the NVIDIA AI stack, on-prem GPU infrastructure, or open-source LLM serving; Contributions to open-source AI/agent projects, relevant publications, or cloud/AI architecture certifications