AI Automation & Workflow Engineer
SeamlessassistPosted Yesterday
Every job listed here is analyzed by our AI to identify worldwide hiring — not just “remote in the US.” Our classification is actively being improved, some results may be inaccurate.
Worldwide Remote
Jobs reviewed for worldwide hiring.
Real Hiring Data
Country flags show the countries where each company has team members
Updated Hourly
Fresh jobs synced from thousands of career pages
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 experience in software development, automation engineering, or AI systems Proficient in Python and/or JavaScript — writes and debugs code independently Hands-on experience with REST APIs, webhooks, and authentication (OAuth, API keys) Experience building with LLM APIs (OpenAI, Anthropic/Claude, or similar) in real projects — not just prompting tools Hands-on experience with at least one automation platform: n8n, Zapier, or Make Strong written English — technical documentation reviewed at screening Advanced AI fluency — AI is embedded in how they design, build, and ship systems Available during client business hours — time zone overlap required Preferred Experience with GoHighLevel (GHL) at the API or workflow level RAG experience — retrieval-augmented generation, vector databases, or persistent memory GitHub portfolio or demo showing an AI agent or automation built and running in production Experience in a client-facing, agency, or embedded role Background in fitness tech, SaaS, or service business operations — understands real business context Familiarity with cloud infrastructure (AWS, GCP, or Azure) for deploying lightweight agents Tools & Platforms LLM APIs: OpenAI (GPT-4o), Anthropic Claude API, Gemini, Mistral Automation: n8n, Zapier, Make (Integromat) Languages: Python, JavaScript (Node.js) APIs & integration: REST APIs, webhooks, OAuth, Postman Vector / memory: Pinecone, Weaviate, Chroma, or similar CRM / ops tools: GoHighLevel, HubSpot, Airtable — at API level Dev tools: GitHub, VS Code, Cursor, GitHub Copilot Cloud: AWS Lambda, Google Cloud Functions, or similar Communication: Slack, Zoom, Google Workspace