Nimburastar Influencer Marketing Agency
Full Stack Engineer, AI Agents — (London)
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Tano
Tano is an AI native influencer marketing company building production AI agents that manage creator partnerships end to end. We are looking for a full stack engineer to own our influencer affiliate management agent from design through to deployment.
What you will do
You will own the influencer affiliate management agent end to end. That means designing, building and shipping a production AI system that manages creator relationships over months without needing to be manually babysat.
What we are looking for
This role is for someone with real, hands on experience, not someone looking to learn on the job.
- Roughly 3 to 6 years of full stack engineering experience
- You must have actually built and shipped AI agents or application layer AI work in a live production environment. Coursework, personal experiments, or exposure to AI tools does not meet this bar
- Comfortable working across TypeScript, Go, Postgres, LangGraph, Gemini, Supabase, Fly.io and Vercel, or able to demonstrate strong equivalent experience
- Based in the UK, or willing to relocate yourself to London. This is a full time, in person role, five days a week in our central London office. We are not able to support visa sponsorship or relocation costs at this time
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Compensation and benefits
- Base salary up to £150k depending on experience
- Up to 0.2% equity
- Health insurance
- Onsite gym
Process
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- The process is quick, usually around two weeks end to end.
- Intro call
- Two 30 minute calls with our CTO and another team member
- Onsite interview in London
Before you apply
If you have not personally built and shipped an AI agent or application layer AI system in production, this role will not be a fit, regardless of general full stack strength. Please only apply if you meet the experience bar above and can commit to working in office in central London five days a week.
If your experience aligns with even some of the stack above, please send your CV through immediately. We would rather review a strong candidate early than miss them because the overlap was not a perfect match.
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