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Tide is a fintech platform serving over 2 million SMEs globally with business banking, invoicing, and accounting solutions. The company has raised over $300 million and operates across the UK, India, Germany, and France with 2,800+ employees.
You will lead the engineering organization behind KYX (KYC and KYB), Tide's identity, onboarding, and continuous due diligence platform. This role unifies engineering leadership across three domains: Onboarding KYX (identity, access, registration, due diligence policy execution, and risk scoring), Continuous KYX (ongoing due diligence and team member onboarding), and Risk Scoring & ML (models powering decisioning across both domains).
Key responsibilities include:
- Own engineering strategy and organization for KYX, unifying previously separate Onboarding and Continuous KYX lines
- Lead distributed engineering teams (backend, frontend, data science, ML engineering) across Hyderabad, Delhi, Sofia, Belgrade, Bucharest, and Vilnius
- Own the KYX Scoring & ML capability as a shared service, managing the full model lifecycle from development through production monitoring
- Scale agentic AI solutions in regulated workflows, aligned with Tide's governance framework
- Collaborate with KYX Ops, Policy, Second Line of Defence, Product, and IAM teams across UK, EU, and India
- Deliver measurable outcomes: higher automated decisioning rates, improved due diligence quality, and reduced KYX cost per member
- Build once, deploy globally, with market-specific logic isolated
This role sits at the intersection of regulated risk operations, member-facing conversion, and applied AI. You will shape how quickly and effectively Tide onboards members, scales into new markets under its own licenses, and leverages AI as a lasting competitive advantage in a regulated environment.
Requirements:
- Experience leading engineering organizations in identity, onboarding, fraud, financial crime, or another regulated decisioning domain
- Strong track record of turning platform investment into measurable outcomes: conversion, due diligence quality, unit economics, and workflow automation
- Experience taking machine learning from development into production at scale and owning model performance post-launch
- Experience operating in regulated environments, working with Ops, Policy, and second-line risk and compliance functions
- Strong judgment across organization design, delivery, platform strategy, data architecture, and technical quality
- Ability to lead distributed teams across multiple markets and unify separate engineering lines behind one roadmap
- Bonus: Experience deploying AI or agentic systems inside regulated workflows with clear governance, evaluation, and human oversight
