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About Duco
Duco is building the agentic Operations platform for financial services. We help firms break the bad data cycle that has held the industry back for decades by handing repetitive, time-intensive data work to AI agents, so their teams can step up to become decision makers. Our no-code platform automates the front-to-back processing of all structured and unstructured data throughout its lifecycle: extraction and classification, transformation, reconciliation and validation, exception management and publication to downstream systems.
Over 150 financial institutions, including 15 tier one banks, rely on Duco to keep their data accurate, their costs controlled, and their operations agile. We now process the equivalent of a billion lines of data almost every day, and are headquartered in London, with offices in New York, Wrocław, Antwerp, and Singapore.
The Role
You will work in one of our cross-functional, empowered engineering teams. Each team owns its part of the product and makes decisions jointly, alongside Product, Sales, and other customer advocates. You won’t just build what someone else specifies—you will create new solutions to difficult problems and help take the platform to the next level.
As a Senior Engineer, you will be a subject matter expert across multiple areas of the platform, involved in cross-team technical design, and independently responsible for the full lifecycle of the projects you deliver. At this level, we expect your impact to reach beyond your own work: lifting your immediate team, and raising the bar across engineering as a whole through technical leadership, reviews, and mentoring.
We also want you to help shape how we build. AI is already core to our product, and it is changing how we engineer software just as fast. We want someone with the curiosity and judgement to help you adopt it well, across how the whole team designs, builds, tests, and ships.
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Working with AI at Duco
We see AI as a genuine lever for how good teams build software, not a gimmick. We have made a start, with much more to build. A growing part of that is harness engineering: designing the environments, constraints, and feedback loops that let AI coding agents work reliably at scale, so engineers shift from writing every line to designing the systems that govern how agents write code.
In this role, you will:
- Use AI tools effectively in your own work, from writing and reviewing code to debugging and documentation.
- Help shape our pipelines and harness: guides, validation loops, test generation, CI/CD, and quality gates that make AI-assisted work reliable for the whole team.
- Bring ideas, and a healthy scepticism for hype, on where AI adds real value and where it does not.
- Champion safe, responsible use that respects code quality, security, data handling, and our engineering standards.
You need not have done all of this before. Curiosity, judgement, and a few real examples will go a long way.
Main Areas of Responsibility
- Deliver features into the core product, across all areas of the technology stack.
- Own the full lifecycle of work: design, development, deployment, and ongoing support.
- Lead spikes and technical design for work that spans more than one team or domain.
- Code review peers’ contributions and give clear, constructive feedback.
- Tackle gnarly data manipulation and scaling problems with a team of very bright people.
- Mentor engineers and lift capability across the wider engineering function, including in how we use AI.
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Requirements
- Experience delivering and supporting resilient software that scales, in a multi-language, multi-technology stack.
- Deep experience with Java or another statically-typed language, and familiarity with a web framework (Spring Boot or Ruby on Rails).
- Strong skills in automated testing (unit, end-to-end, performance, stress) and a high bar for quality.
- Working knowledge of SQL and relational database concepts at scale, including performance optimisation.
- Technical or team leadership experience with practical experience of service-oriented architectures and container-based deployment (Kubernetes, Helm).
- A genuine interest in using AI to improve software engineering, with ideas (and ideally hands-on experience) for applying it individually and across a team's pipeline.
Beneficial
- Solid experience with a dynamically-typed language (e.g. Ruby); JavaScript, TypeScript, and a front-end framework (React).
- Aptitude with CI/CD pipelines (GitLab preferred) and cloud computing, preferably AWS.
- Experience with harness engineering or integrating AI/LLM-based tooling into a workflow or CI/CD pipeline, or with AI-assisted code review, test generation, or developer productivity tooling.
- Experience with architectural migration (e.g. service extraction or refactoring) and feeding observability or metrics systems.
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