Engineering and infrastructure

Applied AI and machine learning engineering

Applied AI and machine learning engineering turns learned models into dependable software that does a real job for real users. AI companies need it because a strong model on its own is not a product. Someone has to build the system around it.

Models are now good enough that most of the hard work sits around them: data flow, evaluation, failure handling and fit with how customers actually work. Demand is forming because more companies are shipping to paying users and finding that prototypes break. These hires are tricky because titles blur. A research profile, a product engineer and a customer-facing builder can all look similar on paper. Ask for production evidence, not demos.

Hiring now 4

Roles in this family

4 roles

Hiring nowMachine Learning EngineerA Machine Learning Engineer turns trained models into tested, dependable services that run inside real products. They make sure model quality holds…Hiring nowAI Application EngineerAn AI Application Engineer builds the user-facing software that joins a model to business logic. They turn model capability into a product that…Hiring nowApplied AI ScientistAn Applied AI Scientist takes a research method and makes it work on a real product problem. They prove it beats a simple baseline on representative…Hiring nowForward Deployed AI EngineerA Forward Deployed AI Engineer works inside a customer's environment to get AI solutions running on real tasks. They matter because a model only…

Where the people come from

Software engineering or applied ML with production evidence

Not to be confused with. Research novelty and enterprise transformation are different outputs.

Core skills

  • Python
  • Deep learning
  • MLOps and model lifecycle
  • Model evaluation

Where it is used

  • Cross-sector AI platforms
  • Enterprise software and developer tools
  • Retail and ecommerce

Routes in and out

People move on to

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