AI/ML Engineer (FinCrime/Fraud Prevention)

💻 Job Title: AI / ML Engineer (FinCrime / Fraud Prevention)

💰 Salary: up to £135k (+80% of salary in equity!)

📍 Location: Central London, EC1 (3 office day/week)

🏦 Company: B2B FinTech / Fraud Prevention

👥 Employees: ~25

💸 Funding: $15m+ (Series A)

This London startup is building a new intelligence layer designed to bring more context and security to digital payments. Their technology analyses transactions in real time, gathering signals from multiple sources to determine whether a payment is legitimate or potentially fraudulent.

The platform combines distributed data systems, real-time investigations and AI-driven decisioning to help financial institutions detect scams while allowing legitimate payments to flow without unnecessary friction. Within 2 years of being founded, they're working with most Tier 1 banks and payment providers in the UK - and are just getting started!

They are now looking for an experienced AI/ML Engineer with deep fraud or financial crime experience (ideally APP fraud exposure) to join at an early stage and help shape the core intelligence powering the platform.

Key responsibilities:

• Designing and deploying machine learning models used to detect fraud and financial crime in payment flows
• Building features from heterogeneous data sources, including transaction data, contextual signals and unstructured information
• Improving systems that extract useful signals from fragmented or unstructured data sources
• Building reliable ML infrastructure to train, deploy and monitor models in production environments
• Working closely with product and engineering teams to ensure models improve real-world fraud outcomes
• Identifying the fraud signals, typologies and data sources that meaningfully improve detection capability
• Experimenting with both classical ML techniques and newer AI approaches where appropriate
• Helping shape data strategy, including how feedback loops and labelling pipelines are built to improve models over time

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This role is focused on shipping production systems rather than academic research.

✅ Must have requirements:

• Strong practical experience building fraud detection systems or financial crime models in production
• Deep FinCrime / FinTech / Payments domain expertise
• Product mindset - focus on improving real-world outcomes, not just model metrics.
• Comfortable working across modelling, data pipelines and production systems.
• Experience working in fast-moving environments where systems are built from scratch and priorities evolve quickly.

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• Experience working with heterogeneous datasets (transaction data, enrichment signals, text, network signals etc.)
• Familiarity with model monitoring, drift detection and retraining pipelines
• Strong SQL and data engineering capability
• Strong programming skills in Python

👍 Bonus points for:

• Exposure to / understanding of APP Fraud
• Previous experience working in an early stage start-up and/or high growth scale up
• Exposure to newer approaches such as LLM-powered systems
• Cloud infrastructure / data platforms experience, ideally GCP

🛂 VISA sponsorship is available if needed.