Reports to: CTO
Location: hybrid, with our London office as the hub, (could consider remote)
Team: Engineering
Type: Permanent, full time
Seniority: We are calibrating this role by demonstrated ability, not just tenure. Scope and level will match the person we hire – we can consider someone earlier in their career with real, provable AI-native and AI-assisted delivery focus and experience rather than a senior engineer without it.
About B4B Payments
B4B Payments, part of the Banking Circle Group, is an award-winning provider of card issuing and embedded payment solutions. We are regulated as an E-Money Institution and hold Principal Membership with Mastercard and Visa, serving over 1,000 corporate clients across the UK, Europe and the US. We build the technology that lets organisations issue cards, manage spend and run payment programmes at scale.
The roles
-We are growing our engineering team and hiring several strong, We are looking for versatilefor a versatile engineers who can own delivery across the full software development lifecycle and who have genuinely adopted an AI-assisted way of working. You will design, build, test and ship software, work fluently across several languages and our AWS platform, and turn ambiguous product direction into working solutions. These are broad, senior engineering roles, not single-language specialist posts.
This is a regulated payments environment governed by frameworks including DORA, PCI DSS, SOC 2, EMI regulations and GDPR. These frameworks shape our SDLC itself, from change control and approvals to testing evidence and audit trails. Delivery here happens within defined controls, and the efficiency we want comes from working smart inside them, not around them. That is not for everyone. We are looking for people who can navigate those controls intelligently and still move fast.
This is a rigorous, hands-on selection process. It is built for engineers who can demonstrate real AI-assisted delivery, not just describe it.
What you will do
- Own features end to end across the full SDLC: design, build, test, deploy and support. Expect to go from a rough product brief to something live in production in days, not weeks or months.
- Work across our polyglot stack, contributing confidently in Ruby, Node and Go, plus frontend work with modern frameworks and web technologies.
- Use AI tooling as a core part of how you build, review and QA software, and help raise the team’s AI-assisted delivery practices.
- Work effectively within a large, complex monolith, including the parts where AI tooling struggles due to size and interdependencies, and find pragmatic ways to stay productive there.
- Use AI-assisted refactoring to actively improve the monolith over time, not only work around it – addressing technical debt in the parts you touch through incrementally, so the codebase gets easier for both humans and AI tooling to work in.
- Improve how the codebase is documented and structured so AI code assistants can be guided effectively, for example context files, architecture notes and structured inline documentation.
- Convert product specifications into precise engineering specifications, building in enough specificity and context that a code assistant can deliver efficiently and reliably, with minimal iteration, rather than being nudged repeatedly toward a solution.
- Use proofs of concept to prove out the right level of specification and methodology, so AI-assisted changes land reliably and with confidence.
- Deliver across a full SDLC that is constrained by regulation, including change management, approvals, testing evidence and audit trails, and apply your AI-assisted workflow within those controls.
- Navigate our regulatory and compliance controls efficiently, and propose ways to improve delivery speed and process without weakening those controls.
- Build security into the SDLC by default, from secure coding and threat modelling to dependency scanning and security review, and apply AI-assisted workflows without compromising the security of secrets, data or the supply chain.
- Build and run services on AWS, using containers on ECS and EKS, with GitHub for source control and CI.
- Work with data at a high standard: data analysis, ETL, and data QA supported by AI tooling.
What we are looking for
- A proven track record as a strong engineer, with genuine full-SDLC experience.
- Demonstrable evidence of converting your own workflow to an AI-driven SDLC. Be ready to show how, not just say that you do.
- Breadth across languages: solid hands-on experience in more than one of Ruby, Node and Go, and comfortable picking up others.
- Good frontend skills and working knowledge of modern frontend frameworks and web technologies.
- Proven experience working in large monolithic codebases, including ones that AI tools find hard to navigate, and the judgement to know where AI helps and where it does not.
- A bias toward improving the codebase you inherit, not just navigating around its limits – comfortable using AI-assisted refactoring to modernise legacy code in place, with a concrete example of having done it.
- Hands-on experience with code documentation techniques that give AI code assistants the context and guidance they need to be productive, such as context and guidance files, architecture documentation and well-structured inline docs.
- Strong product sense: able to translate a product specification into a detailed engineering specification, deciding the level of technical specificity a code assistant needs to deliver efficiently and reliably. This is a skill you can demonstrate through practice and portfolio, not only through years of experience.
- A deliberate AI-assisted delivery methodology: uses proofs of concept to refine the specification and context so changes can be delivered reliably and with confidence, with minimal iteration, rather than by repeatedly nudging the tool.
- A get-stuff-done operator who takes ownership and unblocks themselves. Comfortable being empowered and enabled to make progress, and confident navigating the organisation to get what they need to deliver.
- Fast at absorbing complex, heavily regulated domains and applying that understanding in your delivery.
- Genuine comfort operating in a regulated environment. You understand the controls and the regulation behind them, work within them intelligently rather than treating them as an obstacle, and can propose efficiency improvements that keep compliance intact. Familiarity with, or the ability to rapidly get to grips with, frameworks such as DORA, PCI DSS, SOC 2, EMI regulations and GDPR.
- You understand that the SDLC is constrained by these frameworks and can still deliver efficiently within it, using AI-assisted ways of working that respect change control, evidence and audit requirements rather than bypassing them.
- Security built into how you develop, at an engineering and development level, not just awareness of policy. You can speak to secure coding, threat modelling, dependency and secrets management, and pipeline security, and you understand how to use AI tooling without introducing security or data-handling risk.
- Comfortable across the AWS ecosystem, containerisation (ECS and EKS), and relational databases (MySQL, with some PostgreSQL).
- Strength in data analysis, ETL and data QA, ideally with AI tooling.
Why this role
At B4B Payments we are transforming our engineering ways of working and you would be an instrumental part of a new wave of engineers explicitly hired to work this way – with executive sponsorship, a mandate to help define how the wider engineering organisation builds from here, and real investment in the tooling and time to do it properly.
In the first 90 days we would expect to see real, shipped features delivered through your own AI-assisted workflow, a documented improvement to how part of the codebase is structured or guided for AI tooling, and evidence that you can navigate our regulatory controls without slowing delivery down.
Our environment (what you will work with)
AWS across the stack, GitHub for source and CI, containerised workloads on ECS with EKS for new builds, MySQL and some PostgreSQL, and a polyglot codebase spanning Ruby, Node, Go and modern frontend frameworks. You do not need every item on day one. We care most about strong engineers who learn fast and work well with AI tooling.
Selection process
Our process is deliberately rigorous and short. There is a barrier-to-entry question at application, followed by a maximum of three interviews.
At application
Alongside your CV, submit one written answer: describe, end to end, how AI tools are embedded in your development workflow today, with a concrete example.
Interview 1: Prove it (30 to 45 minutes)
A deep-dive with the hiring manager into real examples from your experience: an AI-driven SDLC you have built, a large monolith you have worked in, and a complex regulated domain you had to learn quickly.
Interview 2: Show us, live (around 90 minutes)
A live, screen-shared session. You will extend a feature using your own AI setup, and complete a short code-review exercise on AI-generated code. We want to see how you drive the tools, how you review and correct their output, and your security instincts.
Interview 3: Final with the CTO (around 60 minutes)
A conversation on ways of working, plus a short exercise where you translate a product specification into an engineering specification suitable for efficient AI-assisted delivery.
NOTE:
We kindly request that you contact us by submitting your CV, indicating your current location, and confirming your eligibility to work in Lithuania. We assure you that our team will promptly respond to your inquiry.
Please note that we are not considering inquiries or applications from Agencies. Additionally, we regret to inform you that we are unable to provide visa sponsorships at this time
