AI Automation
AI Automation Engineer (n8n + Python)
A commercial client needed an engineer to automate internal processes on n8n and Python. We found the person, closed the hire — and more than a year later the engineer is still there, shipping automations.
Client
Commercial client automating internal operations (under NDA)
Challenge
Low-code automation hiring is deceptively hard: the role sits between engineering and operations, and most candidates are either pure developers who dislike low-code tools or no-code enthusiasts who can't write production Python when a workflow outgrows the platform.
Hiring Strategy
We defined the role around outcomes — which processes must be automated and what they cost the business manually — and screened for the rare combined profile: fluent in n8n, solid in Python, and comfortable owning processes end to end.
Search Process
Focused search and screening against practical automation scenarios rather than abstract algorithm questions: real workflows, real integrations, real failure handling.
Result
The engineer was hired and has been working for over a year. The client is happy, the engineer keeps extending the automation footprint — a long-tenure hire is the most honest metric a recruiter can show.
Technologies
- n8n
- Python
- Process automation
- API integrations
Business Impact
Internal processes moved from manual work to maintained automations, with one accountable engineer owning them — for over a year and counting.
Key Takeaways
- Retention is the real hiring metric: a year-plus tenure says more than any interview score.
- Hybrid roles need hybrid screening — we tested both the low-code and the code sides of the job.
- Defining the role through business processes, not tool lists, is what made the match stick.
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