Senior Software Engineer (AI/Data)

Role overview

Build and operate the data and reasoning systems behind an AI assistant that helps banking teams understand business clients. The work spans dependable data pipelines, shared metric definitions, change detection, and AI features that turn reliable financial signals into useful guidance. You’ll shape the product with engineering, product, and commercial partners and remain responsible for systems after launch.

Responsibilities

- Create a semantic layer of measures, dimensions, and definitions so answers remain consistent across users and questions. - Build systems that detect meaningful changes in client data and surface them proactively. - Develop calculations and structures that let language models select reliable financial measures and explain their significance. - Build the agents, tool interfaces, and model evaluations that support an assistant embedded in an existing productivity tool. - Own the path from source data through pipelines, services, and models to the figures presented to users; add tests and observability to catch errors. - Run what you build, investigate failure modes, and help set the direction of the product with partner teams.

Requirements

- Experience building production systems from the ground up, with attention to interfaces, failure handling, maintainability, and testing. - Practical experience serving data to language models and addressing issues such as unclear definitions, weak retrieval, and missing evaluations. - Strong skills in C#, Python, or another backend language, along with experience moving, transforming, or serving data. - Depth in at least one of SQL, Spark, Databricks or Delta Lake, Dagster, Airflow, Temporal, or dbt. - Familiarity with CI/CD, Docker, cloud infrastructure, and observability; ability to reason about whether available data supports a requested answer.

Nice to have

- Experience making data or metrics accessible to models through a metrics layer, metric views, or a tool server. - Experience setting up delivery pipelines or infrastructure for a team, and using AI to automate repetitive parts of engineering work.

Benefits and work setup

- Small, fast-moving team with end-to-end ownership and an emphasis on shipping useful work and improving it. - Fully remote or hybrid work, with no required office schedule and an expectation of sustainable working hours.