AI AI Product & Platform Head

Role & responsibilities

1. Product vision, roadmap & strategy

• Define and own the product vision, roadmap and execution strategy for keeping every decision anchored to the Output-vs-Outcome thesis.
• Translate the four-stage workflow and the Fiverr-style Submitted Needs Correction Accepted model into clear, low-friction experiences for clients, the CLOUDIT team, firms and management.
• Prioritise ruthlessly: ship a thin, working slice first, then expand through a phased rollout.

2. AI platform architecture (LLMs & RAG)

• Design and develop the AI platform architecture: as an aggregator/orchestration layer that routes prompts across multiple LLMs (e.g. ChatGPT, Claude, Gemini, Grok, Perplexity) behind one interface.
• Architect the RAG framework — ingestion, embeddings, vector storage and retrieval — so prompts return accurate, business-specific answers.
• Define the prompt-routing logic, guardrails and fallback strategy to balance quality, latency and cost; avoid single-vendor lock-in.
• Establish token metering and an AI-hours model that feeds billing (token cost + margin + human hours).

3. Workflow & human-validation layers

• Build and integrate the workflow with AI and human-validation layers across all four stages: Pre-Processing, CLOUDIT Review, Firm Review and Client Output.
• Implement the “Satisfied / Need Human Validation” branch, task-level chat (current + archived), the documentsoutputs toggle, and the consultation-booking flow.Build time logging that captures AI-hours, human review hours and call hours at their own rates.

4. Client-level knowledge systems & integrations

• Develop per-client knowledge systems: normalise structured and unstructured data (ERP/accounting, POS, CRM, spreadsheets, meeting notes, owner/employee knowledge) into one unified data structure per client.
• Build and maintain integrations and connectors to common accounting/ERP and POS systems.
• Define how the knowledge base is kept current and continuously improved as new client data arrives.

5. Engineering leadership & delivery

• Lead, mentor and grow the engineering, data and AI teams; set technical standards and ways of working.
• Own delivery: run agile cadences, manage scope and dependencies, and ensure timely, high-quality releases.
• Make pragmatic build-vs-buy and architecture decisions, and own the technical risk register.

6. Cross-functional collaboration

• Work cross-functionally with Operations, partner firms and clients to ground the product in real workflows.
• Enable seamless multi-party collaboration: firm-branded email, in-app chat/notifications, reminders, and VOIP with call recording and transcripts available to firm owners.
• Partner with the CEO and management on pricing guardrails, go-to-market and rollout decisions.

7. Quality, performance & cost efficiency

• Monitor AI output accuracy and define how quality is measured, reviewed and improved over time.
• Own system performance, reliability and security — including the encrypted credential vault, role-based access control and a full audit log.
• Track and optimise AI token cost and infrastructure spend against revenue and margin targets.

8. MVP & pilot execution

• Drive MVP development to a live, demonstrable end-to-end task with one pilot firm and one client.
• Run the pilot, capture feedback, validate billing on a real sample invoice, and convert learnings into the rollout plan.

Preferred candidate profile

• SaaS product development: proven experience taking software products from concept to live, scaled product.
• AI/ML systems: hands-on experience with LLMs, RAG, embeddings/vector stores and AI workflows — not just oversight.
• Architecture & infrastructure: strong command of APIs, cloud infrastructure and modern data systems.
• Business-to-technical translation: able to turn messy business workflows into clean technical solutions.
• Leadership & decision-making: track record leading cross-functional engineering teams with speed and clarity.
• Delivery discipline: experience shipping MVPs and iterating fast under real deadlines.