AI Automation & Workflow Engineer

AI Automation & Workflow Engineer Also placing as:  AI Builder · AI Specialist · Integration Specialist · Workflow Engineer - Data Analyst A senior technical hire who builds AI-powered systems from scratch — writing code, connecting APIs, deploying agents, and making sure everything actually works in production. Field Details Sector AI Engineering · Automation · Systems Integration · Technical Operations Level Senior · 3+ years of hands-on technical experience Education Computer Science degree required (or equivalent technical degree) Coding Required — Python and/or JavaScript minimum AI Fluency Advanced — builds with AI APIs, not just uses AI tools Rate $12–13/hr USD · max TBD Type Full-time · 40 hrs/week or Part time - 20 hrs/week Hours Client's business hours — time zone overlap required Location Remote · Global What They Do Build Automation Systems Design and build end-to-end automation workflows using Zapier, Make, or n8n Connect platforms via REST APIs and webhooks — not just drag-and-drop integrations Write scripts (Python or JavaScript) when no-code tools can't do the job Deploy and monitor systems so they keep running without constant attention AI & Agent Development Build AI agents using OpenAI API, Claude API (Anthropic), or similar LLM APIs Design agent logic — memory, decision trees, tool use, and context management Integrate AI into real business workflows: lead qualification, content generation, support, reporting Use RAG (retrieval-augmented generation) or memory layers when the agent needs to remember things Systems & Integrations Connect CRMs, databases, communication tools, and custom platforms via API Work with tools like Airtable, HubSpot, GoHighLevel, and similar platforms at the API level Handle data flow, error handling, and edge cases — not just the happy path Manage version control via GitHub and keep code clean and documented Documentation & Handover Write clear SOPs and technical guides that non-technical people can actually follow Train client teams to use and maintain what was built Make sure nothing is a black box — every system has documentation AI Tools in Daily Work LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Mistral, Gemini — for building agents and AI features Automation: n8n, Zapier, Make — with custom code steps and API connections Vector databases: Pinecone, Weaviate, Chroma — for RAG and memory systems Dev tools: GitHub, VS Code, Postman — for building, testing, and version control Cloud: AWS Lambda, Google Cloud Functions, or similar — for deploying lightweight agents AI coding assistants: GitHub Copilot, Cursor, Claude for coding — to build faster Data: Airtable, Google Sheets API, SQL basics — for managing structured data Monitoring: simple logging, error alerts, uptime checks to keep systems healthy Requirements Computer Science degree or equivalent technical degree — required 3+ years of hands-on…