AI Project Manager

We are looking for an AI Project Manager to own the delivery of AI agents and applications — from building autonomous AI agents that run securely on NVIDIA Nemoclaw to leveraging cloud AI services such as Azure or AWS where the use case calls for it — from concept to successful deployment. The goal is not simple automation of fixed steps, but true agentic, autonomous AI: agents that can reason, make decisions, and run on their own within real operational workflows. This is a hands-on role: you will define and manage project scope and requirements, work closely with business stakeholders and engineers day to day, and drive projects through build, testing, rollout, and adoption. More than a delivery coordinator, you will act as a change agent who drives process transformation by introducing AI agents into the way the business works — reimagining and reshaping operating workflows around what agents can do, and making sure each AI solution ships on time, meets requirements, and delivers real, transformative value in production. **Project Domain & Scope** ========================== The primary focus of the AI projects is knowledge management for technical support and product R&D — building autonomous AI agents and applications that capture, organize, and surface institutional knowledge so support and engineering teams can work faster and more consistently. These knowledge-management use cases are the first and most important projects this role will help deliver. The role applies a diverse, fit-for-purpose AI toolkit rather than a single approach. Depending on the use case, solutions may run as autonomous AI agents built to run securely on NVIDIA Nemoclaw, or draw on cloud AI services from Azure or AWS — for example, using retrieval-augmented generation (RAG) to deliver lightweight, outward-facing services such as a support-portal chatbot. The emphasis on customer self-service keeps solutions practical and grounded in real-world rollout. Working across this breadth of modern AI technologies, the person in this role will continually apply the latest AI advances to real business scenarios — building durable, in-demand expertise that strengthens the long-term growth and competitiveness of this position. Solutions in this space are commonly built on a modern agent architecture — channels, a central gateway, a plug-ins & skills system, an agent runtime, a memory & knowledge system, an LLM provider, and a local execution environment. We want to make full use of this agent architecture to reach genuinely agentic outcomes: agents that don't just execute predefined scripts, but perceive context, plan multi-step actions, use tools, remember, and operate autonomously as part of the team's day-to-day operating processes. Familiarity with how these pieces fit together is a plus, though deep engineering of them is owned by the AI engineers. A key deliverable is a shared platform that lets users compose and invoke AI agents from a library of reusable skills, design the workflows those agents run, and integrate the growing set of tools agents can call. The aim is to grow this into an AI-agents ecosystem — a common pool of skills and tools that Application Engineers (AE) and R&D teams across Advantech's different product business groups can share and reuse, rather than rebuilding the same capabilities in isolation. Beyond AI projects, this role will also participate in the implementation of other application projects across the IT department — including but not limited to helping establish CI/CD pipelines, Salesforce, and other API application integrations — contributing project management and hands-on delivery support wherever the team needs it. **Key Responsibilities** ======================== * **Scope & requirements —** Own the definition and management of project scope and requirements, translating stakeholder needs into clear, prioritized deliverables and keeping scope controlled as projects evolve. * **Planning & execution —** Build detailed project plans, timelines, and milestones; track progress, manage dependencies and risks, and keep projects on schedule and within scope. * **Stakeholder collaboration —** Partner closely with business stakeholders to gather requirements, align on goals and success criteria, communicate progress, and manage expectations throughout the project lifecycle. * **Hands-on engineering partnership —** Work side by side with AI engineers on a daily basis — clarifying requirements, unblocking issues, reviewing progress, and staying close enough to the technical work to make informed trade-off decisions. * **Deployment & rollout —** Drive AI agents and applications through deployment and rollout, coordinating testing, validation, launch readiness, user onboarding, and post-launch monitoring to ensure solutions are adopted and perform reliably in production. * **Agentic & autonomous outcomes —** Push solutions beyond simple automation toward genuine agency — working with stakeholders and engineers to identify where AI agents can run autonomously within operating workflows, define the guardrails and human-in-the-loop checkpoints for safe autonomy, and steadily expand what agents can own end to end. * **Platform & agent ecosystem —** Help shape and deliver a shared platform where users can assemble AI agents from reusable skills, design their workflows, and plug in the tools agents call — growing a common library of skills and tools into an AI-agents ecosystem that AE and R&D teams across Advantech's product business groups can share and reuse. * **Cross-functional coordination —** Coordinate across functions (product, business units, IT, and other teams) to secure the resources, approvals, and inputs each project needs. * **Documentation & reporting —** Establish and maintain lightweight project documentation, status reporting, and decision logs so progress and decisions are transparent to the team and leadership. * **Broader IT delivery —** Support the delivery of other IT-department application projects as needed, applying the same project management discipline and hands-on approach beyond the AI portfolio. * **Continuous improvement —** Identify process improvements and help mature how the team plans, builds, and ships AI projects over time. **Key Goals and Success** ========================= In this role, success means AI projects that ship on time and within a well-managed scope, stakeholders who feel heard and informed, engineers who are unblocked and focused, and AI agents that don't just automate isolated tasks but run autonomously and reliably inside real operating workflows — trusted enough that the team hands them meaningful work end to end. **Required Qualifications** =========================== * 3–5 years of experience in project management, technical program management, or a comparable delivery role, ideally involving software, data, or AI/ML products. * Demonstrated ability to define and manage project scope and requirements, and to keep complex projects on track from kick-off to launch. * Strong stakeholder management and communication skills, with a track record of aligning business and technical teams. * Comfort working hands-on and closely with engineers; able to understand technical concepts, ask the right questions, and make sound trade-off decisions. * Experience taking a product or application through deployment and rollout, including testing, launch, and adoption. * Organized, proactive, and able to manage multiple priorities in a fast-moving environment. * Bachelor's degree in a relevant field, or equivalent practical experience. **Preferred Qualifications** ============================ * Exposure to AI, machine learning, or LLM-based agents and applications, ideally including building autonomous AI agents and running them on secure runtimes such as NVIDIA Nemoclaw. * Experience with knowledge management, technical support, product R&D, sales, or marketing workflows and the systems that support them. * Familiarity with modern software delivery practices (Agile/Scrum, sprint planning, backlog management) and tools such as Jira or similar. * Experience working with cloud AI services (e.g., Azure or AWS) and techniques such as retrieval-augmented generation (RAG), and understanding of how AI solutions are deployed and monitored in production. * Project management certification (e.g., PMP) is a plus but not required.