AI Product Engineer Equity at Godiligent

AI Product Engineer

£70k-£110k + Equity

London, UK

Go diligent.ai is a Y Combinator‑backed fintech infrastructure startup in London building AI agents to automate Anti Money Laundering operations. Backed by top‑tier investors and currently at post‑seed stage with strong traction, the company is revolutionizing how banks and fintechs handle tedious compliance tasks and financial crime investigations.

As an AI Product Engineer , you will bridge the gap between engineering, product, and AI design. You’ll own core AI agent infrastructure, building serverless Type Script systems on AWS to automate L1 compliance tasks. This high‑impact role involves shipping features rapidly, making architectural decisions, and working directly with founders and customers.

• Direct impact on fighting financial crime by automating millions of manual KYC/AML tasks using cutting‑edge AI agent technology.
• Join a Y Combinator‑backed startup with strong post‑seed runway, production customers, and founders with deep fintech and AML domain expertise.
• Experience extreme ownership and rapid iteration cycles where you build on Monday, test on Tuesday, and see customer impact by Friday.

• Design and deploy core AI agent infrastructure, utilizing LLM workflows, tool calling, and human‑in‑the‑loop validation systems for financial compliance.
• Build and maintain critical integrations with legacy KYC/AML platforms that banks rely on, ensuring secure and scalable financial crime workflows.
• Collaborate daily with the CEO and CTO to translate customer pain points into production‑ready features within a serverless AWS environment.

• 1–5 years of experience building production systems using JavaScript/Type Script, AWS serverless architectures, and modern CI/CD practices.
• Proven technical excellence via a top‑tier university degree, experience at elite tech firms like Meta or Stripe, or growth in a VC‑backed startup.
• Strong AI‑native building intuition with practical experience in prompt design, structured outputs, and developing reliable agent‑based evaluation loops.