Job Title

Company ContentJet is a performance creative and UGC company helping brands produce high-performing short-form video ads at scale. We are currently transforming from a service-led UGC company into a tech-enabled creative intelligence company. We have already built an internal AI platform that helps our team collect project briefs, run market research, identify competitors, analyze app store reviews, find winning ads, generate creative strategy, produce scripts and storyboards, and analyze submitted ads. The platform is chat-based, with product-specific chats that allow users to ask questions about a specific client/product and generate creative outputs. We are now looking for a senior AI Product Architect / Fractional CTO to audit what we have built, assess its scalability, and help us define the next stage of the platform. About the Project We already have an internal engineer who built the first version of the platform. The platform currently supports: - Project brief collection - AI-assisted market research - Competitor discovery - App store review analysis - Winning ad discovery and scraping - Creative strategy generation - Product-specific AI chat - Script generation - Storyboard generation - Ad analysis - Follow-up research and creative recommendations We are not looking for someone to rebuild everything from scratch. We are looking for someone who can help us answer one key question: Is the current platform technically strong enough to scale into ContentJets internal AI operating system? What We Need We need a senior AI product and technical architect to conduct a structured audit and create a clear technical and product roadmap. You will review the current platform and help us understand: - What is solid - What is fragile - What needs to be refactored - What should be built next - What data structure we need - How the agents should be organized - How to improve output quality - How to prevent hallucinations - How to structure source tracking - How to make the system scalable for internal team use - Whether we need another engineer, an AI QA lead, a data engineer, or a product manager next Key Responsibilities 1. Platform Architecture Audit Review the current technical architecture of the platform. This includes: - Backend structure - Frontend and user flow - Database structure - Chat and product memory logic - Agent and workflow design - API usage - LLM integration - Scraping and research logic - Data storage and retrieval - Scalability - Maintainability - Cost efficiency - Security and permissions The goal is to help us understand whether the current platform is built on a strong enough foundation to support future growth. 2. AI Agent and Workflow Review Assess how the current AI workflows are structured. This includes reviewing: - Brief intake flow - Research workflow - Competitor analysis workflow - App review analysis workflow - Winning ad discovery workflow - Strategy generation workflow - Script generation workflow - Storyboard generation workflow - Ad analysis workflow - Product-specific chat memory - Prompt structure and versioning - Human approval points - Failure handling We want to know whether the agents are structured properly, whether the workflows are scalable, and where the current system may break as more users and features are added. 3. Data Model Review Help us design the right data model for a creative intelligence platform. We need guidance on how to structure and store: - Client briefs - Product information - Competitors - App reviews - Customer pain points - Customer objections - Winning ads - Creative angles - Concepts - Hooks - Scripts - Storyboards - Ad analysis - Creator data - Project data - Performance data The long-term goal is for every project to generate reusable creative intelligence that improves future strategy, scripts, creator matching, and performance analysis. 4. AI Output Quality and Reliability Review Help us define how to evaluate whether the AI is producing useful, accurate, and high-quality outputs. We need recommendations for: - Output scoring systems - Research relevance checks - Hallucination detection - Source validation - Script quality evaluation - Strategy quality evaluation - Storyboard quality evaluation - Ad analysis quality evaluation - Prompt regression testing - Human feedback loops This is especially important because the platform is not just a technical tool. It needs to produce creative and strategic outputs that our team can trust. 5. Product Roadmap Create a clear 30/60/90-day roadmap. The roadmap should tell us: - What to fix first - What to build next - What to stop building for now - What can stay as-is - What needs to be refactored - What integrations matter most - How to connect the platform to Monday.com, Slack, Google Drive, CRM, or performance data later - What role we should hire next We want a practical roadmap that helps us make smart decisions with our budget and avoids unnecessary overbuilding. 6. Hiring and Team Recommendation Advise us on the next technical and product hires. We are currently considering roles such as: - AI Product QA Lead - AI Systems Lead / Full-Stack AI Automation Engineer - Data engineer - Automation engineer - Technical product manager - Additional full-stack developer We need your recommendation on who should come next based on the audit, not assumptions. Expected Deliverables At the end of the engagement, we expect: - An architecture audit report explaining what is strong, weak, risky, or missing in the current platform - A data model recommendation for how to structure briefs, research, ads, scripts, performance data, creator data, and project data - An agent workflow review covering the current AI flows and how they should be improved - An AI quality framework for evaluating research, strategy, scripts, storyboards, and ad analysis - A security and permissions review covering client data protection, team access, and role-based permissions - A clear 30/60/90-day roadmap for what to build, fix, or refactor next - A hiring recommendation explaining which technical or product role we should hire next and why Ideal Candidate You should have experience with: - AI product architecture - LLM-based applications - AI agents or multi-step AI workflows - RAG / retrieval-augmented generation - Vector databases - Prompt and workflow design - SaaS or internal tool architecture - Backend systems - Data modeling - API integrations - AI QA and evaluation frameworks - Product strategy - Working with non-technical founders or operators - Reviewing code and advising internal engineers Bonus if you have experience with: - Creative tech - Marketing technology - Adtech - UGC or creator platforms - Performance marketing - Workflow automation - Monday.com, Slack, Google Drive, or CRM integrations - Scraping and research pipelines - Internal AI tools for teams Engagement Structure We are looking for a short, focused engagement first. The initial engagement should last 46 weeks. The expected workload is around 510 hours per week, with heavier involvement during the first one or two weeks. You will work directly with: - The CEO - Our internal engineer - The creative/strategy team - The operations lead - Our future AI QA/Product Lead We want someone who can challenge our thinking, review what we have built, and give us a practical roadmap. We are not looking for generic AI theory. We need practical, technical, product-driven guidance. What We Are Not Looking For We are not looking for someone who: - Immediately recommends rebuilding everything without auditing the current platform - Only writes prompts - Only gives high-level consulting with no technical depth - Wants to build a full SaaS from scratch before validating the internal system - Pushes us to train our own AI model unnecessarily - Has no real experience with AI product architecture - Cannot review architecture, data models, workflows, and engineering decisions - Cannot communicate clearly with both technical and non-technical people