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Specialisms - AI Engineering - Fintech
Fintech is where applied ML earns its keep: fraud detection, credit decisioning, transaction intelligence and risk modelling. Re:Sourced recruits the ML engineers who have shipped these systems inside regulated environments, where model governance and auditability are part of the job, not an afterthought.
The profile
Fintech ML work is dominated by four problem classes: fraud and anomaly detection at transaction speed, credit risk modelling under regulatory scrutiny, customer intelligence (churn, LTV, next-best-action), and increasingly LLM-based automation in onboarding and compliance operations.
The differentiating capability is model governance. Engineers who have operated models inside APRA-regulated or AFSL-holding environments understand explainability requirements, audit trails and the gap between a notebook AUC score and a defensible production system. That experience is scarce and prices at the top of the AI band.
Where they come from
The deepest pools sit inside the payments and lending scale-ups (Airwallex, Zeller, Athena, Block) and the big-four model risk teams. Candidates moving from bank model-risk roles into scale-ups are a reliable pattern: they bring governance discipline and want faster shipping cycles.
Senior fintech-experienced ML engineers in Sydney price at AUD 180-220k base, top of the standard AI band, base only, 25th-75th percentile of accepted offers. See the full bands in the Salary Guide 2026.
Submit a brief
30 minute working session. No fee until placement. Replacement inside 90 days.