Senior Software Engineer (AI/Data)

This role builds the data and reasoning systems behind an AI assistant that answers bankers' questions about business clients. A strong candidate has built production systems from scratch, served data to a language model and seen where it fails, and is comfortable owning pipelines, definitions and tests end to end. Depth in one data tool plus a backend language matters more than a data engineer title.

How it fits the AI era

The product itself is an AI assistant, so the work is about giving a model reliable ground truth to reason over rather than configuring a warehouse. Agents handle repetitive work while engineers take the hard parts and run what they ship. What stays human is judgment about definitions, failure modes and knowing when the data cannot support a number.

How it builds AI experience

A hire would gain hands on work with a Codat side model, tool servers for an assistant, and evals that catch wrong answers, all on real small business financial data. Building the semantic and signal layers teaches how to make model output consistent and trustworthy. That experience is directly relevant to Codat's advisory platform for commercial banking teams.

Worth showing

• Built production systems from scratch
• Served data to LLMs
• Backend coding in C# or Python
• SQL or Spark or dbt depth
• CI/CD and cloud observability

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Codat

Codat provides an advisory intelligence platform connecting small business financial and accounting data for commercial banking teams, according to the company.