Senior AI Engineer
<p><b>Salary: £48,000 - 60,000 per year</b></p>
<b>Requirements:</b>
<ul><li>Strong experience shipping applied ML into production (not just experimentation)</li><li>Strong Python + ability to write maintainable, tested code</li><li>Strong SQL + comfort working directly with messy, high-volume data</li><li>Solid modelling judgement: calibration, leakage, bias, thresholding, cost trade-offs, monitoring/drift</li><li>Experience building decisioning systems where reliability, latency, and explainability matter</li><li>Experience in fraud/scams, payments, risk, trust & safety, AML, or adjacent domains (nice-to-have)</li><li>Familiarity with graph/network features and entity resolution style problems (nice-to-have)</li><li>Experience with MLOps tooling (model registry/MLflow, feature stores, orchestration) (nice-to-have)</li><li>Comfort with cloud-native/event-driven systems and working closely with platform/backend engineers (nice-to-have)</li><li>Experience integrating unstructured signals (text/embeddings/RAG style pipelines) into decision systems (nice-to-have)</li></ul>
<b>Responsibilities:</b>
<ul><li>Build and ship scam risk models and signals (typology classification, risk scoring, decision logic)</li><li>Engineer features across heterogeneous data: transaction context, behavioural sequences, counterparty signals, network/graph patterns, and unstructured evidence</li><li>Design calibrated outputs (scores + reason codes) that are actionable and explainable for banking workflows</li><li>Own evaluation end-to-end: leakage avoidance, cost-sensitive metrics, thresholding, phased rollouts, and post-incident learning</li><li>Productionise ML: packaging, deployment, monitoring, drift detection, and retraining strategies</li><li>Collaborate closely with backend/product teams to integrate intelligence into real-time payment flows</li><li>Work alongside agent/LLM workflows for evidence gathering and synthesis, while keeping the decision core predictable and auditable</li></ul>
<b>Technologies:</b>
<ul><li>Backend</li><li>Cloud</li><li>Flow</li><li>LLM</li><li>Network</li><li>Python</li><li>SQL</li><li>Security</li><li>AI</li></ul>
<p><b>More:</b></p>
<p>We are a high-growth fintech company building a payment intelligence layer for banks. Our goal is to run real-time investigations on payments to provide rich context on the counterparty and situation, intercepting scams while ensuring genuine payments flow smoothly. As an early-stage team, we are moving fast and tackling problems where correctness, security, and reliability are non-negotiable. By joining us, youll work on a mission with real-world impact and have the opportunity to make meaningful technical decisions.</p>
<p>last updated 8 week of 2026</p>
<b>Requirements:</b>
<ul><li>Strong experience shipping applied ML into production (not just experimentation)</li><li>Strong Python + ability to write maintainable, tested code</li><li>Strong SQL + comfort working directly with messy, high-volume data</li><li>Solid modelling judgement: calibration, leakage, bias, thresholding, cost trade-offs, monitoring/drift</li><li>Experience building decisioning systems where reliability, latency, and explainability matter</li><li>Experience in fraud/scams, payments, risk, trust & safety, AML, or adjacent domains (nice-to-have)</li><li>Familiarity with graph/network features and entity resolution style problems (nice-to-have)</li><li>Experience with MLOps tooling (model registry/MLflow, feature stores, orchestration) (nice-to-have)</li><li>Comfort with cloud-native/event-driven systems and working closely with platform/backend engineers (nice-to-have)</li><li>Experience integrating unstructured signals (text/embeddings/RAG style pipelines) into decision systems (nice-to-have)</li></ul>
<b>Responsibilities:</b>
<ul><li>Build and ship scam risk models and signals (typology classification, risk scoring, decision logic)</li><li>Engineer features across heterogeneous data: transaction context, behavioural sequences, counterparty signals, network/graph patterns, and unstructured evidence</li><li>Design calibrated outputs (scores + reason codes) that are actionable and explainable for banking workflows</li><li>Own evaluation end-to-end: leakage avoidance, cost-sensitive metrics, thresholding, phased rollouts, and post-incident learning</li><li>Productionise ML: packaging, deployment, monitoring, drift detection, and retraining strategies</li><li>Collaborate closely with backend/product teams to integrate intelligence into real-time payment flows</li><li>Work alongside agent/LLM workflows for evidence gathering and synthesis, while keeping the decision core predictable and auditable</li></ul>
<b>Technologies:</b>
<ul><li>Backend</li><li>Cloud</li><li>Flow</li><li>LLM</li><li>Network</li><li>Python</li><li>SQL</li><li>Security</li><li>AI</li></ul>
<p><b>More:</b></p>
<p>We are a high-growth fintech company building a payment intelligence layer for banks. Our goal is to run real-time investigations on payments to provide rich context on the counterparty and situation, intercepting scams while ensuring genuine payments flow smoothly. As an early-stage team, we are moving fast and tackling problems where correctness, security, and reliability are non-negotiable. By joining us, youll work on a mission with real-world impact and have the opportunity to make meaningful technical decisions.</p>
<p>last updated 8 week of 2026</p>