Hiring nowApplied AI and machine learning engineering

AI Application Engineer

Also called Generative AI Engineer · AI Software Engineer

An AI Application Engineer builds the user-facing software that joins a model to business logic. They turn model capability into a product that completes real tasks and fails safely, which is what customers actually judge.

What they do

They design the application around the model: how requests are shaped, what context the model sees, which steps are handled by code and which by the model, and what happens when an answer is wrong. They write Python services, connect to model APIs, and build evaluation sets that run on every change. They add safeguards such as validation, fallbacks and human review for risky actions. They watch real usage, read failed cases, and fix the workflow, the prompt or the data. They work closely with product and design.

What a strong hire delivers

In the first months, a strong hire ships a working AI feature that real users complete tasks with. It has an evaluation set the team trusts, a clear record of how often it fails and how badly, and safeguards on the actions that matter. They also leave behind a repeatable way to test changes, so the team can improve the product without guessing.

How you can tell they are good

Ask them to walk through an AI application they shipped. Look for how they measured task completion, which failures were consequential, and what they changed because of it. Ask how they chose between code and the model for a step. Strong people show their evaluation work and talk openly about what broke. Be wary of demos with no failure data.

Why it is hard to fill

The title is used loosely. Some candidates are software engineers who have only called an API in a prototype. Others are machine learning people who have never run a user-facing service. The useful hire sits across both and has production evidence. Few have shipped something with real evaluation and safeguards, and those who have are rarely looking, so search needs to be targeted and the brief needs to be precise.

Where to find them

They tend to come from backend or full-stack software teams that moved into model-based features, and from applied machine learning engineers who started owning product code. Look at startups building agent frameworks or AI-first products, product engineering teams at software companies, and teams that run model evaluation. Titles vary: generative AI engineer, AI software engineer, ML engineer, or simply senior software engineer.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Application Engineer

  1. Tell me about an AI application you took to production. What did it do and what broke first?

    A strong answer is specific about the task, users and first real failure, and what they changed. Worry if they describe only the demo, blame the model for everything, or cannot say how they knew it was failing.

  2. How did you decide whether a step should be handled by code or by the model?

    Good answers weigh reliability, cost, latency and testability, and favour deterministic code where it suffices. Worry if they default to the model for everything or cannot give a case where they removed it.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building AI-powered software that helps customers complete real tasks, and we need an engineer to own how the model fits into the product. You will design the application, measure how well it works, and make it fail safely. You will work with product and design from the start.

What you will do

  • Design and build user-facing features that combine model calls with business logic
  • Build and maintain evaluation sets and run them on every change
  • Add safeguards such as validation, fallbacks and human review for risky actions
  • Integrate model APIs and manage model versions and changes
  • Review real usage and failed cases, then improve workflows
  • Track task completion and consequential failure rate and report on them
  • Work with product and design on what to build and what to hold back

What you will bring

  • Production software written in Python, with evidence you can point to
  • Shipped at least one application that uses a model in a live product
  • Experience building evaluation for model-based behaviour
  • Sound judgement about when to use code and when to use a model
  • Clear understanding of deep learning basics and the model lifecycle
  • Ability to explain failures and trade-offs plainly to non-engineers

Nice to have

  • Experience with agent frameworks
  • Familiarity with PyTorch and model registries
  • Experience with MLOps practices for monitoring and rollout

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • PythonAdvancedDevelop tested numerical and data software
  • Deep learningAdvancedBuild and diagnose trainable neural models
  • MLOps and model lifecycleWorkingVersion, release, monitor and retire models
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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