DeployedJobs

Applied AI Engineer Jobs

Applied AI Engineers build customer-facing AI and LLM solutions for specific use cases — where deployed engineering meets frontier AI. The title is most common at the frontier labs and at the platform companies putting models into an existing product, and it is the fastest-growing of the six titles on this board. Every posting is classified from its own text rather than its title, so what you see below is the work, not the label.

105 verified roles
Page 1 of 6Next →

What an Applied AI Engineer actually does

An Applied AI Engineer builds the model-facing part of a product or deployment: retrieval, evaluation, agent design, tool use, and the scaffolding that turns a capable model into something reliable enough to put in front of users.

The title emerged as frontier models moved from demonstrations into production, and it usually means applied rather than research work. Training and fine-tuning appear in some postings, but the centre of gravity is systems engineering around a model somebody else trained — and increasingly, the evaluation discipline that tells you whether the system actually works.

Many of these roles are customer-facing, which is why they sit on this board. Where a posting is purely internal product work with no customer contact, it is a different job wearing a similar title, and the classification reflects that.

Day to day

  • Designing retrieval and context strategies against real customer data
  • Building evaluation harnesses, then using them to decide what shipped works
  • Prompt and agent design, including tool use and multi-step workflows
  • Wiring models into existing product surfaces and data pipelines
  • Working directly with a customer team on what the system needs to do

What hiring teams screen for

  • Python
  • LLM application patterns — RAG, agents, tool use, structured output
  • Evaluation design and error analysis
  • Vector search and embeddings
  • API integration and backend engineering
  • Data pipelines
  • Cloud deployment
  • Judgement about what a model can and cannot be trusted to do

Common questions

Do I need a machine learning research background?
Usually not. Most Applied AI Engineer postings want strong software engineering plus real judgement about model behaviour, not publications. Research-track roles tend to be titled Research Engineer or Research Scientist.
How is this different from a Forward Deployed Engineer?
Overlapping and converging. The distinction that survives is emphasis: an Applied AI Engineer is defined by the model-facing work, an FDE by the embedded customer relationship. Plenty of postings are genuinely both, and carry both labels here.
Is fine-tuning part of the job?
Sometimes, but less often than candidates expect. Retrieval, evaluation, and prompt or agent design carry more of the load in most production systems.

Related roles