AI Developer Job Description Templates (Ready to Use, 2026)

Most AI developer job descriptions fail for the same reason: they ask for everyone. They want someone who can build n8n automations, ship a React frontend, write Python services, and fine-tune a transformer, all in one person, at one salary. That person is rare, expensive, and usually not what the job actually requires. A good job description starts by being honest about which of those things you need, then describes that role precisely.

So instead of one bloated template, here are three. Each one maps to a real tier of work, with the responsibilities and tools that belong to it. Use them as is or as a starting point. If you want to skip the writing and just get the person, here is how to hire an AI engineer without the usual months of sourcing.

Template 1: AI Automation Specialist

This is the entry point. An automation specialist does not write production application code or train models. They connect existing tools so that work happens without a human in the loop: lead routing, data sync, report generation, AI-assisted workflows. If your goal is to remove manual steps and ship internal automations fast, this is the role.

Role summary

We are hiring an AI Automation Specialist to design, build, and maintain automated workflows that connect our tools and use AI to handle repetitive tasks. You will own the automation stack end to end, from mapping a manual process to shipping a reliable workflow that runs without supervision.

Responsibilities

• Build and maintain automations across our core tools using n8n, Make, or Zapier.
• Connect CRMs, forms, spreadsheets, and messaging tools into reliable end to end workflows.
• Use LLM APIs to add AI steps to workflows, such as summarizing, classifying, or drafting content.
• Set up and manage GoHighLevel pipelines, triggers, and follow up sequences.
• Document every workflow so a non technical teammate can understand what it does and when it runs.
• Monitor automations, fix breakages quickly, and add error handling so nothing fails silently.
• Identify manual processes across the team and propose where automation will save the most time.

Requirements

• One or more years building automations in n8n, Make, or Zapier.
• Comfort working with APIs, webhooks, and JSON without needing a full software background.
• Hands on experience using an LLM API inside a workflow.
• Strong logical thinking and the patience to debug a workflow until it is reliable.
• Clear written communication for documentation and handoffs.

Nice to have

• Experience with GoHighLevel or another marketing automation platform.
• Basic Python or JavaScript for custom function steps.
• Familiarity with prompt design for consistent LLM outputs.

Tools

n8n, Make, Zapier, GoHighLevel, OpenAI or Anthropic APIs, Airtable or Google Sheets, webhooks and REST APIs.

Template 2: AI and Software Developer

This is the builder. An AI and software developer ships AI features inside a real product: a chat interface, a retrieval system over your documents, an LLM powered feature in your existing app. They write production code on both the frontend and backend, and they treat the model as one component in a working system, not the whole job. If you have a product and want AI inside it, this is the role. You can see how we scope this hire on our AI software developer page.

Role summary

We are hiring an AI and Software Developer to build and ship AI powered features in our product. You will work across the stack, from the user interface to the backend services that call language models, and you will own features from prototype to production.

Responsibilities

• Build user facing features in React and supporting backend services in Node or Python.
• Integrate LLM APIs into product features, including chat, search, and content generation.
• Design and implement retrieval augmented generation, or RAG, over our own data and documents.
• Set up vector storage, embeddings, and the retrieval logic that feeds context to the model.
• Write prompts and guardrails that keep model outputs accurate, on brand, and safe.
• Handle the unglamorous parts: rate limits, caching, cost control, evaluation, and error states.
• Ship, measure, and iterate, rather than building a demo that never reaches users.

Requirements

• Three or more years of professional software development.
• Strong React on the frontend and Node or Python on the backend.
• Hands on experience integrating LLM APIs into a production application.
• Experience building or maintaining a RAG pipeline, including embeddings and a vector store.
• Solid grasp of APIs, databases, and deploying services that real users depend on.

Nice to have

• Experience with frameworks such as LangChain or LlamaIndex.
• Familiarity with evaluation tooling for LLM output quality.
• Cloud deployment experience on AWS, GCP, or Azure.

Tools

React, Node.js, Python, OpenAI and Anthropic APIs, a vector database such as Pinecone, Weaviate, or pgvector, LangChain or LlamaIndex, Git, and a cloud platform.

Template 3: Machine Learning Engineer

This is the specialist. A machine learning engineer trains, fine tunes, and deploys models. They are not just calling an API, they are building or adapting the model itself and putting it into production reliably. This role is the most expensive and the most specialized, so only write this job description if you genuinely need custom models or fine tuning, not if an LLM API would do. The average machine learning engineer in the United States earns about $128,769 per year according to ZipRecruiter, which is one reason teams increasingly hire this role embedded and offshore. Our hire a machine learning engineer page walks through that option.

Role summary

We are hiring a Machine Learning Engineer to design, train, deploy, and maintain machine learning models in production. You will own the full model lifecycle, from data preparation and training through deployment, monitoring, and retraining.

Responsibilities

• Build, train, and evaluate models using PyTorch or TensorFlow.
• Fine tune foundation models on our data for our specific use cases.
• Prepare, clean, and version datasets for training and evaluation.
• Deploy models to production and build the serving infrastructure around them.
• Own MLOps: pipelines, experiment tracking, model versioning, and automated retraining.
• Monitor models in production for drift, latency, and accuracy, and retrain when they degrade.
• Work with product and engineering to turn model output into features users can rely on.

Requirements

• Three or more years building and deploying machine learning models in production.
• Strong Python and deep learning with PyTorch or TensorFlow.
• Hands on experience fine tuning models, not only calling pretrained APIs.
• Solid MLOps practice, including pipelines, experiment tracking, and model deployment.
• Comfort with the math behind the models and the engineering behind serving them.

Nice to have

• Experience with distributed training and GPU optimization.
• Familiarity with tools such as MLflow, Weights and Biases, or Kubeflow.
• A track record of moving a model from notebook to reliable production service.

Tools

Python, PyTorch, TensorFlow, Hugging Face, MLflow or Weights and Biases, Docker and Kubernetes, a cloud platform with GPUs, and a feature store or data pipeline tooling.

How to write an AI job description that actually works

The templates above will get you most of the way, but a few habits separate a job description that attracts the right person from one that wastes everyone’s time.

Pick one tier and commit. The single biggest mistake is asking one person to cover all three roles above. Decide whether the core work is automation, application development, or model training, and write for that. You can list the others as nice to have, but the role has to have a center of gravity.

Describe the work, not a wish list of tools. A list of twenty technologies tells a candidate nothing about what they will do all day. Lead with the outcomes you need shipped, then name the tools that serve those outcomes. Candidates who have done the work will recognize themselves.

Be honest about seniority and pay. If you need someone to fine tune models and own MLOps, that is a senior, expensive hire, and the job description should say so. Posting a senior role at a junior band just fills your inbox with the wrong applicants.

Separate must have from nice to have. Every requirement you mark as essential shrinks your pool. Keep the must have list short and real, and push everything else into nice to have. Good people self select out when they see a wall of mandatory requirements they happen to be missing one of.

Skip the posting and hire the role embedded

Writing the job description is the easy part. Sourcing, screening, and onboarding the person is where months disappear. That is the gap Ad Snipper closes. We place embedded, dedicated AI talent that works inside your team and your tools, vetted and onboarded for you, with a free replacement if the fit is wrong, all white label under your brand.

The three templates above map directly to our three tiers. An AI automation specialist starts at $15 per hour, an AI and software developer at $25 per hour, and a machine learning engineer at $35 per hour. On a full time monthly basis that is $2,400, $4,000, and $5,600. You can hire AI engineers across any of these tiers and have a working person embedded in days, not the quarter it usually takes to fill an AI role yourself.

Frequently asked questions