Full Time, Fully Remote role - AI Automation Engineer - Digital Marketing Agency

Jobs
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I’m working with a London-headquarted digital marketing agency who are looking to bring on their first AI Automation Engineer.

Fully Remote, GMT +/- 3
£40,000 - £45,000
Interviews are happening now

Requirements: Python, JavaScript, exposure to building LLM agents & AI integrations, familiar with building automation tools in Zapier/ Make/ n8n or similar

Full details can be found here and happy to answer any questions. hire|py - AI Automation Engineer

Connect with me on LinkedIn:

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Hi there,

Congratulations on the growth of your digital marketing agency and for prioritising automation so early. Hiring an AI Automation Engineer is a smart move and the role description already covers several best-practice capabilities.

A few considerations that often make these projects successful:

  1. Standardise data hand-offs between your marketing stack (ad platforms, CRM, reporting) before layering advanced AI workflows. Clean, well-structured data makes LLM agents far more reliable.
    1. Build small, testable automations first (lead qualification, campaign performance alerts) and use those quick wins to inform a wider roadmap.
    1. Maintain version control and logging for each workflow so your marketing team can troubleshoot without an engineer.
      Two quick questions to help refine the approach:
      • Which core marketing platforms (e.g. HubSpot, GA4, Meta Ads) will need the deepest automation ties in the first 90 days?
      • Do you have an existing cloud environment (AWS, GCP, Vercel, etc.) for hosting n8n, or should the engineer propose an architecture from scratch?

Happy to share more detailed examples or a phased blueprint once I know a bit more about your stack and priorities.

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Hi Arsema, congratulations on building out your agency’s automation team! Noticed you’re looking for an AI Automation Engineer who has hands-on experience with LLMs and platforms like n8n, Zapier, and Make—excellent priorities for scaling your client delivery.

To drive the most immediate value, most teams find a phased approach works: Start by mapping your core lead gen and reporting processes, then automate data syncs between your ad platforms, CRM, and reporting tools in n8n. Use quick-win automations (like lead enrichment or campaign alerts) to build trust and get rapid feedback from the team before rolling out anything complex.

Could you share which marketing channels matter most in the next quarter—paid social, email, organic, or all? Also, do you already have internal technical talent who’d help own or support the new automation infrastructure, or will this be a standalone hire?

Happy to share a sample blueprint or discuss common blockers if that’s helpful!

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“Looking to bring on their first AI Automation Engineer” is an exciting milestone. With n8n you can set up a modular workflow that automatically collects campaign data, enriches it with AI insights, and pushes results to your reporting dashboards – all without bulky custom code.

Here’s a simple three-layer approach you could trial:

  1. Data Capture: Use the native HTTP node to pull marketing metrics from Google Ads, Meta, and your CRM on a scheduled trigger.
    1. AI Enrichment: Pass the raw numbers to an OpenAI node that classifies trends (e.g., rising CPC, high-ROI segments) and returns concise commentary for your team.
    1. Distribution & Alerting: Route the AI summary and raw data into Slack, Google Sheets, and an email digest, adding error-handling branches so nothing is missed if an API call fails.
      A couple of quick questions to refine this: Which channels drive most of your ad spend today, and do you already have a preferred BI tool for final dashboards? Answers will help shape node choices and data formats.