Full-Stack Engineer - Data, Infrastructure & AI

About Agent F Agent F is building an autonomous finance function: software that takes on the work of keeping a company’s finances in order. That means bringing information together from different systems, recording transactions correctly, preparing useful reports, and helping people understand what needs their attention. Today, much of this work depends on spreadsheets, repeated data entry and people checking one system against another. Our ambition is to make that work increasingly automatic, while keeping every number explainable and every consequential action under clear control. We are building towards a complete business management system for growing companies. These founding roles will shape both the product and the way we build it. About the role You will be a full-stack engineer, shipping customer features across the interface, application services and database. Your particular strength will be the data and infrastructure that make those features dependable: how records are stored, how imports and background jobs run, and how information moves between Agent F and a customer’s other tools. You will help build and operate the whole product, with deeper ownership of this foundation. This is hands-on full-stack engineering. A typical piece of work could involve extending a data model, implementing an application service, connecting it to a screen, testing it against a real database and following the release into production. You will also investigate slow reports, failed imports and jobs that stop halfway through, then improve the system so it handles those situations reliably. That foundation also supports our AI work. As we add document extraction, connected customer knowledge and software that takes actions for customers, you will build the services and data access those features need. The immediate priority is dependable software that customers can use, with more capable automation developed around specific tasks. What you will build and improve • Application services and customer features. Build the logic and application programming interfaces (APIs) that connect records, business rules and screens. Take features through implementation, testing, release and production support. • The database foundation. Design clear data models, enforce relationships and transaction boundaries, and improve slow queries. Manage schema changes through reviewed migrations, preserve financial history and keep each customer’s data isolated. • Imports and integrations. Connect accounting tools, business systems and spreadsheets to a consistent model. Validate incoming records, handle changing source formats, and check that imported totals and reports agree with the source. • Reliable background processing. Build scheduled jobs and workers that process work outside a user request. Handle retries without creating duplicates, resume interrupted runs, and make progress and failures visible to the team. • Performance and recovery. Diagnose expensive queries, processing bottlenecks and partial failures. Test changes against real databases, plan for growing workloads, and build procedures to recover processing safely with the platform engineer. • Practical AI automation. Add features such as extracting invoice fields or preparing an order from approved business data. Give models access through controlled services, check the result and require approval where needed. Develop connected customer knowledge when a feature needs those relationships, and measure whether it improves the result. For example, a large customer import stops halfway through and the same records arrive again on the next run. You would make processing resume safely, prevent duplicate records, verify the resulting totals and expose the job’s status in the product. You would also