Software Engineer

Every AI call in DataSnipper goes through us. Product teams do not talk to model providers directly, they talk to our gateway. We are responsible for how inference is routed, how it fails over, what it costs, and how anyone can tell whether the output is any good.

The second half of the job is evaluation. We are building the platform teams use to measure AI quality: versioned datasets, experiment tracking, and evaluation runs they can act on. It is a hard problem and largely an open one, so you will have real influence over how we solve it.

This is a small team with a large blast radius. You will own real production systems, set the standards other teams build against, and see your work in front of hundreds of thousands of users in audit and finance.

About DataSnipper

Audit and finance are still massively manual and we are changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms.
Now, we are taking things further with our Excel Agent, bringing AI directly into where the work actually happens. Unlike generic AI tools, we do not sit on the sidelines. Our AI operates inside Excel, with access to real documents and audit evidence, meaning it does not just generate answers, it does the work, with full traceability.
We are not just applying AI, we are redefining how audit gets done. If you want to build something category-defining at scale, this is the place.

What you will do

Technical Delivery

• Own the LLM gateway: routing, provider failover, rate limits, retries, and cost attribution across multiple model providers

• Deploy, version and deprecate models across clouds, regions and environments, including quota and capacity planning, managed as infrastructure as code

• Build the shared evaluation platform: versioned datasets, experiment tracking, run and result schemas, reporting, and trace linkage back to the run

• Own the infrastructure for async and long-running AI workloads

Reliability, Security & On-Call

• Own observability for AI traffic: latency, retries and fallbacks, token usage, cost and errors, per team and per use case

• Take part in the on-call rotation, run incidents, and close the follow-ups

• Implement the security and compliance controls the platform is held to: retention, access control, RBAC and SSO

Collaboration & Impact

• Define and maintain clean integration contracts between the platform and the teams that consume it

• Partner with product and ML engineers to turn their requirements into platform capabilities that are self-service rather than a request queue