Hiring nowApplied AI and machine learning engineeringPart of The agent workforce

Forward Deployed AI Engineer

Also called Forward Deployed Engineer, AI · AI Deployment Engineer · Forward Deployed Engineer (FDE) · Applied AI Engineer · Deployment Solutions Engineer

A Forward Deployed AI Engineer works inside a customer's environment to get AI solutions running on real tasks. They matter because a model only earns its place once it works in the customer's systems.

What they do

They sit close to the customer and learn how the work actually gets done. They find the systems, data and handoffs the AI has to fit into. Then they build the integration, usually in Python, connecting model calls to customer APIs and internal tools. They prototype quickly, test against real cases, and fix what breaks. Much of the job is debugging in systems they did not design. They set up evaluation so the customer can see whether the model helps. They run staged rollouts, feed lessons back to the product team, and document everything so the customer's own people can run it.

What a strong hire delivers

In the first months, a strong hire gets one customer deployment live and working on a real task. They leave behind measured rollout evidence showing what improved and what did not. They hand over cleanly, so the customer's team can operate the integration without them. They also bring back patterns from the field that the product team can reuse.

How you can tell they are good

Ask them to walk through a deployment they took from first meeting to handover. Listen for the messy parts: awkward customer systems, unclear data, a failure they diagnosed. Ask how they measured success and how they decided to widen or stop a rollout. Strong people name trade-offs and show real evaluation work. Be wary of polished demos with no production evidence.

Why it is hard to fill

Very few people combine solid software engineering, applied ML sense and comfort sitting with a customer. The titles vary widely, so searching by job name misses good people. Demand has grown sharply, and labs, startups and large deployment-led firms all compete for the same small group. Ex-founders and consultants who moved into engineering often convert well, but they are rarely labelled correctly.

Where to find them

Look at full-stack and backend engineers who have shipped LLM features in production. Applied ML engineers with customer exposure are another source. Solutions engineers and technical consultants who now write real code fit too, as do former founders. Typical employers are AI labs, AI startups and deployment-heavy software companies that embed engineers with clients.

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 a Forward Deployed AI Engineer

  1. Tell me about an AI integration you built inside a customer's environment. What was hardest?

    A strong answer is specific about the customer's systems, constraints and what went wrong. It shows how they debugged and what they changed. Worry if the story is only about the model, or if the customer barely appears.

  2. How did you decide whether a deployment was working for the customer?

    They should describe task-level measures agreed with the customer, a baseline, and evaluation on real cases. Worry about vague claims of happy users, or metrics that only measure the model rather than the customer's outcome.

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

Job description you can post

We put AI to work inside our customers' own operations. As a Forward Deployed AI Engineer, you will work directly with customer teams, build integrations into their systems, and prove with evidence that the solution improves their work. You will also feed what you learn back into our product.

What you will do

  • Learn how a customer's work and systems operate before building anything
  • Build and ship integrations between models and customer systems
  • Prototype quickly and test against real customer cases
  • Design evaluation that measures task outcomes
  • Run staged rollouts and report results clearly
  • Debug problems across customer data, APIs and model behaviour
  • Hand over documented, supportable solutions and share patterns with our product team

What you will bring

  • Production software engineering work you can show and explain
  • Strong Python and experience building against APIs
  • Shipped LLM or applied ML features, with evaluation evidence
  • Experience debugging unfamiliar systems under time pressure
  • Clear communication with technical and non-technical customers
  • Judgement about when to build, when to wait and when to say no

Nice to have

  • Experience with model registries and model lifecycle practice
  • Background in consulting, solutions engineering or founding a company
  • Experience with deep learning frameworks such as PyTorch

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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