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 b