AI Systems Architect / Data & Automation Engineer

**AI Systems Architect / Data & Automation Engineer** **Build the AI Operating System for a Multi-Million-Dollar E-Commerce Company** **About Us** Katia Designs is a multi-million-dollar DTC jewelry brand running operations across Shopify, Meta, Google, Klaviyo, Amazon, TikTok Shop, wholesale, affiliates, customer service, operations, and finance. We're hiring an AI Systems Architect / Data & Automation Engineer to build the infrastructure behind an AI-driven company. This is not a ChatGPT install or a handful of Zapier automations. We want a centralized intelligence system that understands how the entire company runs. **The Vision** We're building a central AI operating system for Katia Designs. Step one: a data warehouse and intelligence layer that collects, normalizes, and structures information from across the company, then makes it accessible to AI. Data sources include Shopify, Meta Ads, Google Ads/Analytics, Klaviyo, Amazon, TikTok Shop, affiliate platforms, wholesale/Faire, customer service, inventory, finance, Slack, Google Drive/Sheets, and internal SOPs. Beyond data collection, we want every action our team takes to feed the company's institutional knowledge. We want to capture the full chain: data, human action, context, decision, result. Over time this tells us what happened, who decided it, why, what data informed it, what action followed, and what the system should learn from it. The end goal: specialized AI agents that take over increasingly complex work currently done by human operators, such as Meta Ads, Google Ads, email/SMS, affiliate management, CRO, customer service, inventory planning, financial analysis, executive reporting, and growth strategy. **Phase 1: Build the Foundation** You'll architect and build the core data and intelligence infrastructure. This may involve BigQuery, cloud infrastructure, ETL/ELT pipelines, APIs and webhooks, structured event logging, data modeling, dbt or similar transformation tools, n8n/Make or other automation infrastructure, vector databases, LLM APIs, RAG architecture, AI agent frameworks, and MCP or similar integration layers. We're not locked into a specific stack. We want your recommendation. **Phase 2: Capture Human Intelligence** Next, we build mechanisms to capture the context behind our team's decisions. Example: a Meta buyer adjusts a campaign. The system should eventually record the before state (performance and KPIs), the decision (what changed), the context (why they changed it), the after state (what happened next), and the learning (did it improve performance). We want this framework applied company-wide, creating a growing decision and knowledge layer that AI systems can reference and learn from. **Phase 3: Build AI Agents** Once the data and knowledge infrastructure is solid, we build specialized agents. Agents start at observe, analyze, recommend. Then observe, analyze, recommend, execute with human approval. Eventually, where it makes sense: observe, decide, execute, measure, learn. Human oversight, permissions, audit trails, and safeguards are core to the architecture, not an afterthought. **Who We're Looking For** Real-world experience in several of these areas: - Data warehouse architecture, BigQuery or equivalent - API integrations and ETL/ELT pipelines - E-commerce data architecture, Shopify APIs, marketing platform APIs - LLM APIs, RAG systems, vector databases - AI agents and workflow automation - Event-driven architecture and data governance - AI evaluation/monitoring and human-in-the-loop systems Experience with Shopify, DTC, or e-commerce businesses is strongly preferred. **What We're Not Looking For** Skip this role if your background is mainly prompt engineering, basic ChatGPT wrappers, simple Zapier automations, generic chatbots, or reselling an off-the-shelf "AI transformation" package. We need someone who designs and builds underlying architecture, not someone who assembles pre-built tools. **Initial Engagement** The first phase is a paid architecture and discovery engagement. We'll give you access to our tech stack, reporting, systems, and key team members. Deliverables for this phase: 1. Audit our current systems and data sources 2. Map how information moves through the company today 3. Identify which systems should feed the central data warehouse 4. Recommend the technology architecture 5. Design the initial data model 6. Design how human actions, decisions, and outcomes get captured 7. Recommend the AI/knowledge architecture 8. Build a phased implementation roadmap 9. Estimate development time and ongoing infrastructure costs 10. Identify the first 2 to 3 AI agents to build, ranked by ROI and feasibility If this phase succeeds, we want you to lead or personally build the implementation. This has the potential to become a significant long-term engagement. **Before You Apply** Skip the generic AI-generated proposal. Answer these directly: 1. If you were building a centralized AI/data operating system for a Shopify company doing millions in annual revenue, what would your proposed architecture look like? 2. What would you use as the central data warehouse, and why? 3. How would you capture human operators' decisions and actions so they become usable institutional knowledge for future AI agents? 4. How would you structure the system so an AI agent could observe a KPI, decide on an action, execute it through an API, and measure the result? 5. What AI agent or agentic system have you personally built and deployed? Describe one in detail. 6. What's your experience integrating Shopify, Meta, Google, Klaviyo, Amazon, or similar platforms? 7. What would you build in your first 30 days with us? 8. What's the biggest technical mistake companies make when attempting something like this? Include examples of systems you've personally architected or built. We're looking for someone who can build the foundation of an AI-native company, not bolt AI tools onto an existing one.