AI Product & Platform Head

Role & responsibilities: 1. Product vision, roadmap & strategy Define and own the product vision, roadmap and execution strategy for Smartask 2.0 and Celia, 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: Celia 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. SmarTask workflow & human-validation layers Build and integrate the Smartask 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 Skills: 812 years of experience, with hands-on work in LLMs, RAG, embeddings & vector stores Hands-on software development experience is a must — this is a builder's role, not a purely strategic one Proven SaaS journey from concept to scaled product Strong grip on APIs, cloud infra & modern data systems Track record leading cross-functional engineering teams Comfort shipping MVPs fast, under real deadlines