Senior AI Applications Engineer

Job Description Summary

We are looking for a full-stack engineer who has built production applications, and who has also helped build software that other engineers then built on top of — a shared library, an internal tool, a reusable component set or a software development kit — establishing engineering standards through automation rather than documentation.
The role combines application development with internal platform and developer-tooling engineering, and the two feed each other: building a real application end to end is how we discover which capabilities the platform should own.
On the platform side you will build GE HealthCare's internal AI engineering framework — the shared libraries, code generation tools, standardised interfaces and automated verification our data scientists and engineers use to take their own AI solutions into production. It exists to deliver four outcomes for the company:

• reduce time from working prototype to production from months to days;
• allow data scientists to ship production-grade AI without expertise in cloud infrastructure, deployment, application security or front-end development;
• make security, compliance and consistency automatically enforced properties of every solution, rather than outcomes dependent on scarce expert review;
• allow underlying technologies to be replaced — model provider, agent framework, user interface approach — without rewriting the solutions built on them.

On the application side you will develop selected AI applications end to end — front end, back end and agents — for priority business use cases, and for the reference implementations that prove each platform capability before it is offered to others. Expect the balance to sit somewhat more on the platform side than the application side, and to shift as the platform matures. It is not an infrastructure operations role.tex

Job Description

GE HealthCare's Chief Data and Analytics Office delivers data, insight and AI products across Finance, Commercial, Supply Chain, Quality, Manufacturing and Operational Excellence.

Most people creating AI solutions here are data scientists and analysts rather than career software engineers. Their modelling, evaluation and domain expertise is what these programmes need; deep infrastructure and front-end expertise is not reasonable to require of them. Every solution must nonetheless reach production as a secure, reliable, supportable enterprise system. A defining characteristic of our approach is that correctness is established by automated tooling — type systems, generated code, schema validation, pipeline checks — rather than by expert human review, which does not scale to the pace required. Your users are colleagues, and your work is measured by how much they accomplish correctly and independently.