AI Automations Engineer

About the role

Are you obsessed with data, partner success, action taking, and changing the game? If you have a whole lot of hustles and a touch of nerd, come work with Pattern! We want you to use your skills to push one of the fastest-growing companies headquartered in the US to the top of the list. About Pattern Pattern (Nasdaq: PTRN) accelerates brands on global ecommerce marketplaces using proprietary technology and AI. Leveraging 46+ trillion data points and advanced machine learning models, Pattern optimizes and automates key levers of ecommerce growth-including advertising, content management, logistics and fulfillment, pricing, forecasting, and customer service. Hundreds of global brands use Pattern's platform to drive profitable growth across 60+ marketplaces such as Amazon, Walmart, Target, eBay, Tmall, TikTok Shop, JD, and Mercado Libre. Learn more at pattern.com. Recognition - Deloitte Technology Fast 500 (North America) honorees. - Inc. Best-Led Companies. - Newsweek's Global Most Loved Workplaces. As a AI Automations Engineer, you will collaborate with business units to identify high-impact, repetitive processes and deliver AI-powered workflow automations that save time, reduce costs, and improve quality for our day-to-day ecommerce trading and operations - using tools we already have (n8n, MCP, Zapier). You'll own design, build, operation, adoption, performance uplifts and ROI for these workflows, with engineering collaboration only when hardening, security, or platform-level integration is required. Most automations are short to mid-term (from few weeks to 6 months) solutions that deliver quick and measurable wins; high impact workflows or projects may be graduated and hardened as a bullet-proof solution over time. This is a full-time role based in Hong Kong (Hybrid). Reporting into Head of Product Management, APAC What is a day in the life of a AI Automations Engineer? - Build and operate AI workflows and agentic patterns using tools like n8n, MCP, and Zapier (flows, prompts, connectors, guardrails). - Run discovery with business units/departments to target repetitive/manual processes; size impact (time saved, error reduction, cost/run). - Manage an intake queue and collaborate with the head of the department to prioritize a backlog/short roadmap by impact, effort, and risk; ship in small increments (pilot, measure, iterate, scale). - Create lean Automation Specs (one-pagers) with triggers, inputs/outputs, data sources, edge cases, fallback/rollback, SLAs, and a simple flow diagram and test checklist. - Operate production automations: monitor health, triage incidents, manage quotas/limits, and optimize costs (batching, caching, provider/routing strategies). - Train junior team members - assign and review intake triage, documentation, QA/UAT, data checks, and training assets; set weekly goals; provide feedback and skills development in SQL, metrics, and workflow tools. - Train and enable business units/departments: documentation, SOPs/runbooks, training videos, workshops/office hours to drive adoption and self-serve where appropriate. - Advise teammates building their own flows; share best practices for the foundational AI layer, guardrails, and evaluation. - Configure and extend internal AI tools (e.g., Open WebUI, workflow plug-ins) and identify successful experiments to graduate or deprecate. - Engage Engineering for platform needs (e.g., new/custom connectors, SSO/permissions, sensitive data integration, observability, or hardening at scale) with concise one-page requests. - Track and report outcomes: hours saved, cost per run, success/error rates, latency/quality, adoption/utilization, MTTR, and net ROI. What will I need to thrive in this role? - 3 to 5 years' experience in business process re-engineering, data analytics, software engineering or enterprise automation roles hands-on experience with low/no-code tools or GenAI is a must. - Language: Fluency in English and Mandarin (written and spoken) is required; - Obsession with learning about LLMs and AIs, plus judgment on when not to use them (AI as an accelerant, not a crutch). - Hands-on experience with n8n, Zapier, Make, MCP, or similar; comfortable building, testing, instrumenting, and maintaining automations. - Data skills: SQL required; basic Python preferred (R/Scala familiarity is a plus) to query data, validate outputs, and instrument flows. - Understanding of core software/ops practices: versioning, change control, monitoring/alerting, and basic CI/CD concepts. - Sound familiarity with cloud/data concepts (e.g., AWS S3/DynamoDB, REST APIs, webhooks) and how they influence workflow design. - Working knowledge of LLMs and agentic systems (RAG/vector search, prompt design, evaluation, safety); Western model families and strengths: OpenAI GPT-5 or 4o, Anthropic Claude, Google Gemini, Perplexity; Microsoft Copilot. - China counterparts and specializations: Alibaba Qwen/Wan, ByteDance Doubao, Moonshot Kimi, Baidu ERNIE/Wenxin, Zhipu GLM, iFly

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