An AI developer builds software that learns from data. They take a business problem, pick or train a model that can solve it, and ship that model into a product people actually use. A good AI developer job description says which problems the person will own, which tools they will work in, and how you will judge their work, so the right candidates apply and the wrong ones self select out.

This page gives you a copy ready AI developer job description template, a breakdown of the role, and a short guide to checking whether a candidate can do the work before they reach a hiring manager’s calendar.

Quick answer

An AI developer designs, trains, tests, and deploys models that use machine learning algorithms, neural network architectures, natural language processing, or computer vision inside real applications. Most roles ask for strong Python, hands on experience with deep learning frameworks, cloud deployment, and a bachelor’s degree in computer science or equivalent practical experience.

What an AI developer does

Artificial intelligence (AI) work has moved out of research labs and into product teams. That shift changed the job. Most companies hiring today want someone who can take an existing model, adapt it to their data, and keep it working in production, not someone who invents new architectures from scratch.

In a typical week, an AI developer might:

• Clean and label training data, then build pipelines that keep it fresh
• Choose between a classic model, a fine tuned open model, or a hosted large language model for a given feature
• Train and evaluate models, tracking accuracy, latency, and cost
• Wrap models in APIs so application developers can call them
• Monitor models in production for drift, bias, and failures
• Explain results and tradeoffs to product managers who do not read code

The work is part software engineering, part applied statistics. Candidates who are strong on only one side tend to struggle. A great researcher who cannot write maintainable code will stall at deployment. A great backend engineer who does not understand evaluation will ship a model that looks fine in a demo and fails on real users.

AI developer job description template

Copy this AI developer job description template into your ATS and edit the bracketed parts. Keep the responsibilities list honest. If the role is mostly integrating hosted models, say so; you will get fewer applicants and better ones.

Job title

AI Developer (also posted as Artificial Intelligence AI Developer, AI Software Developer, or Applied AI Developer)

About the role

We are hiring an AI developer to build and ship [product area, for example document search, fraud detection, or customer support automation]. You will work with product, data, and platform teams to turn models into features our customers use every day. This is a [full time / contract], [remote / hybrid / onsite] role reporting to [title].

Responsibilities

• Design, train, and evaluate models using machine learning algorithms suited to the problem, from gradient boosted trees to deep neural network architectures
• Build and fine tune natural language processing features such as search, classification, summarization, or retrieval augmented generation
• Build computer vision features such as image classification, object detection, or document parsing [remove if not relevant]
• Prepare, clean, and version training and evaluation datasets
• Deploy models to production through APIs or batch jobs on [AWS / Azure / GCP]
• Monitor model quality, latency, and cost, and retrain when performance drops
• Write tests, documentation, and code reviews that other engineers can follow
• Apply responsible AI practices, including bias checks, privacy controls, and clear model documentation

Requirements

• Bachelor’s degree in computer science, data science, mathematics, or a related field, or equivalent practical experience
• [2 to 5] years of software development experience, with at least [1 to 2] years building machine learning or AI features
• Strong Python skills and working knowledge of PyTorch or TensorFlow
• Experience with large language models, prompt design, or fine tuning
• Familiarity with SQL, data pipelines, and version control
• Experience deploying models on a major cloud platform, with containers and CI/CD
• Clear written and spoken communication with non technical partners

Nice to have

• Experience with vector databases and retrieval systems
• MLOps tooling for experiment tracking and model registries
• Published work, open source contributions, or a portfolio of shipped AI features

Key skills for an AI developer

Job postings list a lot of skills. These are the ones that separate strong candidates in practice.

Technical skills

• Python and software engineering fundamentals: clean code, testing, debugging, and working in a shared codebase
• Machine learning algorithms: knowing when a simple model beats a deep one, and why
• Deep learning: building and training neural network models, and reading the loss curve when training goes wrong
• Natural language processing: tokenization, embeddings, transformer models, and evaluation for text tasks
• Computer vision: convolutional and vision transformer models, image preprocessing, and augmentation
• Data handling: SQL, feature engineering, and spotting leakage between training and test sets
• Deployment: APIs, containers, cloud services, and monitoring

Working skills

• Framing a vague request into a measurable problem
• Explaining model limits honestly, including when AI is the wrong tool
• Weighing accuracy against speed, cost, and privacy

Every technical skill on that list is testable. That matters, because resumes for AI roles are crowded with the same framework names, and a keyword match tells you very little about depth.

AI developer vs AI engineer vs machine learning engineer

Titles overlap, and companies use them loosely. Here is how they usually split:

• AI developer: builds AI features into applications. Focus on integration, product behavior, and shipping.
• AI engineer: a broader title. Many AI engineers own the full system around a model, including infrastructure, evaluation, and safety. Some companies use “AI engineers” and “AI developers” as interchangeable titles.
• Machine learning engineer: focuses on training, scaling, and serving models, often with deeper infrastructure and MLOps work.
• Data scientist: focuses on analysis, experiments, and statistical modeling; may not ship production code.

If you are unsure which title to post, write the responsibilities first. The title should follow the work, not the other way around.

How to assess AI developer candidates

Writing the AI developer job description is the easy part. The hard part starts when applications arrive. AI roles attract high volumes of applicants, many of whom list the same tools. Some have shipped models to production. Many have followed a tutorial.

A practical screening process looks like this:

1. Start with a short technical skill test. Use tasks that mirror the job: debug a training loop, pick an evaluation metric for an imbalanced dataset, or write a function that calls a model and handles failures. Glider’s AI development skill test and wider technical skill tests cover these areas, and the Glider question library spans more than 250 technologies.
2. Test in a real environment. Multiple choice questions cannot show whether someone can work in a notebook. Glider assessments support live coding in a WebIDE, Jupyter Notebooks for data science tasks, and coding simulations that run like a small project.
3. Decide your policy on AI assistants. Your AI developer will use coding assistants on the job, so banning them in a test measures the wrong thing. Glider’s AI assistant lets candidates use AI help on coding tasks while recording which prompts they used, so you can see how they think, not just the final answer.
4. Protect the integrity of the result. Remote technical hiring brings proxy test takers and copied code. AI proctoring flags tab switching, pasted code, and face mismatches as data for a recruiter to review, not as automatic rejections.
5. Go deeper in the interview. Use structured questions from the AI developer interview questions library, and run a live coding interview where the candidate walks through a design choice and defends it.

The goal is simple: by the time a hiring manager meets a candidate, the basic questions are already answered. Can this person code? Do they understand evaluation? Are they who they say they are?

Hiring checklist

• Responsibilities describe the actual work, not a wish list
• Requirements separate must haves from nice to haves
• The job title matches the responsibilities
• A role specific skills test is ready before the posting goes live
• Scoring rubric agreed with the hiring manager
• Interview stages and owners confirmed

FAQs

Next step

Once your AI developer job description is live, the screening load arrives fast. Browse the Glider job description library for related roles, or see how the AI development skill test helps you shortlist AI developers on proven skill before the first interview.