Senior AI Engineer

About the role

Most of what we ship with AI in it is a retrieval problem, an evaluation problem and a permissions problem wearing a model on top. You will work on client systems in healthcare, finance and logistics where somebody has to be able to explain, months later, what the system saw and what it decided. If your instinct on hearing that is to reach for an evaluation harness before a prompt, this is the right role.

What you will do

• Design and build retrieval pipelines over client data, including the unglamorous work of getting the index right before touching the model.
• Write evaluation harnesses that turn a client specification into a score, so that done means something you can point at.
• Take ownership of an AI feature end to end: data boundaries, inference cost, latency budget, failure behaviour and rollback.
• Sit on client calls and explain, in plain language, what the system will and will not do. You will sometimes be the person saying no.
• Review other engineers’ work, and have yours reviewed. Nothing merges on one pair of eyes.

What we need from you

• You have put something with a model in it into production and lived with it afterwards.
• Strong Python, and enough backend engineering to own the service the model sits behind.
• You can explain why a retrieval change beat a model change on a system you actually worked on.
• Comfortable with the compliance conversation: what data may leave the environment, and what has to be logged.
• You write clearly. Half this job is making a technical position legible to a non-technical client.

Useful but not required

• Experience with regulated data, particularly HIPAA or PCI scope.
• You have run a model self-hosted, and know what that actually costs.
• Open source work, a paper, or a write-up we can read.