AI-Assisted Software Engineer for Fintech

We're hiring versatile senior engineers who own delivery across the full SDLC and have genuinely adopted an AI-assisted way of working. You'll turn ambiguous product direction into production software in days, not weeks, inside a regulated payments environment (DORA, PCI DSS, SOC 2, EMI regulations, GDPR). Our controls shape the SDLC, from change management and approvals to testing evidence and audit trails. We want engineers who work smart within them, not around them. The selection process is hands-on: be ready to show your AI-assisted delivery, not just describe it.

What you'll do

Own features end to end: design, build, test, deploy and support. - Work across a polyglot stack (Ruby, Node, Go) plus modern frontend. - Use AI tooling as a core part of building, reviewing and QA, and help raise the team's practices. - Stay productive in a large monolith, and use AI-assisted refactoring to improve it incrementally. - Make the codebase easier for AI assistants to work in (context files, architecture notes, structured docs). - Turn product specs into precise engineering specs, using proofs of concept to find the level of detail that lets AI-assisted changes land reliably with minimal iteration. - Deliver within regulatory controls, and propose ways to speed up delivery without weakening them. - Build security in by default: secure coding, threat modelling, dependency and secrets management, and AI workflows that protect data and the supply chain. - Run services on AWS (ECS/EKS) with GitHub for source control and CI. - Handle data analysis, ETL and data QA, supported by AI tooling. What we're looking for - Strong full-SDLC engineering track record, with hands-on experience in at least two of Ruby, Node and Go, and good frontend skills. - Demonstrable evidence of moving your own workflow to AI-driven delivery, with a concrete example of AI-assisted refactoring in a legacy monolith and the judgement to know where AI helps and where it doesn't. - Experience writing documentation and context that guides AI code assistants. - Strong product sense and a deliberate methodology for specifying work, shown through practice or portfolio. - Ownership and self-direction: you unblock yourself and navigate the organisation to deliver. - Comfort in a regulated environment: you understand the reasoning behind the controls, learn new frameworks quickly, and deliver efficiently without bypassing change control, evidence or audit requirements. - Solid AWS, containerisation, MySQL (some PostgreSQL), and data analysis/ETL skills.