全职 AI Prototyping Coordinator - Project Manager 工作, 薪水, Amaris Consulting 公司招聘中 - Ricebowl

Job Description

We are looking for an AI Prototyping Coordinator / Project Manager to sit at the intersection of business teams and technical delivery. This role owns the coordination, scoping and hands-on build of AI prototypes and proof-of-concepts (POCs), turning business ideas into working demonstrations that can be tested with users and, where successful, handed off for full-scale engineering. The role does not require deep AI research expertise, but does require the ability to build functional prototypes (no-code/low-code, Python, or via AI application builders), manage a project end to end, and coordinate effectively across business and IT stakeholders.

About The Job

Data & Requirements Coordination

• Maintain a working inventory of relevant data sources, systems and formats needed to support AI prototyping initiatives, in coordination with IT and data teams.
• Gather and document business requirements from stakeholders, translating loosely defined ideas into clear prototype briefs and user stories.
• Coordinate access to internal and external data sources needed for prototype development, working with data owners on extraction and usage.

AI Prototype Development

• Build functional AI prototypes and POCs (chatbots, assistants, simple RAG/search tools, automation workflows) using no-code/low-code platforms, off-the-shelf AI application builders, and basic scripting (Python) where needed.
• Assemble simple retrieval-augmented generation (RAG) demos connecting a knowledge base or document set to an AI model, using existing tools and platforms rather than building infrastructure from scratch.
• Configure and test AI application builders, low-code agent platforms and reporting/BI tools to support rapid prototyping.
• Iterate on prototypes based on stakeholder and user feedback, prioritising speed and clarity of demonstration over production-grade engineering.

Project & Stakeholder Management

• Own the end-to-end project plan for each prototype: scoping, timeline, milestones, resourcing and status reporting to business and IT stakeholders.
• Act as the day-to-day point of contact between business units, IT/engineering teams and, where relevant, external vendors or consultants supporting the build.
• Organise and facilitate working sessions, demos and feedback loops, including human-in-the-loop testing with end users before wider rollout.
• Prepare clear documentation, status updates and decision materials (slides, one-pagers) for steering committees and sponsors.

Testing, Governance & Handover

• Define lightweight success criteria for each prototype (accuracy, usefulness, adoption) and track results through user testing.
• Flag data privacy, security and responsible-use considerations to IT/security teams, ensuring prototypes stay within approved guardrails.
• Prepare handover documentation and a business case for prototypes that are validated and ready to move into a formal engineering backlog for productionisation.
• Maintain a repository of prototypes, learnings and reusable components to accelerate future initiatives.

Tools & Analytics Support

• Support the selection and evaluation of AI/data tools and platforms (BI tools, data visualisation, AI app builders) suited to business needs.
• Provide first-level support and training to business users on prototype tools once deployed.

About You

• Bachelor’s degree in Computer Science, Information Technology, Engineering, Business, or a related field.
• 2–5 years of experience in project coordination, project management, business analysis, or a related role, preferably with exposure to data, digital, AI, or automation initiatives.
• Hands-on experience developing or configuring AI/automation prototypes using no-code/low-code platforms, AI application builders such as Copilot Studio or Power Platform, and/or basic Python scripting.
• Good working knowledge of AI and LLM concepts, including prompting, RAG, and AI agents, with the ability to scope, assess, and support prototypes without requiring deep machine learning expertise.
• Strong stakeholder management and communication skills, with the ability to collaborate effectively across business and technical teams.
• Highly organised and capable of managing multiple workstreams simultaneously, with a practical, iterative approach to delivery.