AI Engineer Job Description Template
Free AI engineer job description templates: general, founding, senior, applied AI/ML, and generative AI/LLM. Download 5 variations as one DOCX.
AI Engineer Job Description Templates
5 free templates by level and specialization. Download as DOCX or copy-paste.
The AI engineer is one of the most in-demand and fastest-changing roles in tech, and the job description has to keep up. A founding engineer building a startup's AI core, a senior engineer leading system design, and a generative AI engineer working with large language models all share the title but do very different work. Most templates online give you one generic version, which leaves a hiring team with a posting that misses the level, the specialization, and the stack that actually define the role.
At FirstHR, we build for growing companies and the people who run their hiring, and an AI engineer hire is a high-stakes one: the talent is scarce, the pay is high, and a vague posting loses strong candidates fast. The five templates below cover the role by level and specialization: general, founding, senior, applied AI/ML, and generative AI/LLM. Fill in the brackets and post. For the principles behind any posting, the guide to writing a job description covers the fundamentals.
What Does an AI Engineer Do?
An AI engineer designs, builds, and deploys AI systems in production, developing or integrating models, connecting LLMs through APIs, building pipelines, owning performance and monitoring, and collaborating with product and engineering teams. The role increasingly leans toward applied, production work rather than pure research. The closest federal occupation is computer and information research scientists, used as the nearest mapped category.
For the employer writing the posting, the key point is that the work depends on the level and specialization. A founding engineer owns everything end to end; a senior engineer leads design; an applied engineer ties models to product metrics; a generative AI engineer builds on LLMs. The five templates on this page split by level and specialization so the summary and duties match the actual role.
AI Engineer Duties and Responsibilities
AI engineer responsibilities center on models and AI, engineering, production and MLOps, and collaboration. The specialization shifts the emphasis, LLMs and RAG for one role, classical modeling for another, but these four categories hold across nearly every AI engineer role. These are the duties grouped the way the templates use them.
A strong posting grounds these in your specifics: the level, the specialization, the stack, and what the role will own. For a structured way to scope any role before posting, the guide to defining job responsibilities walks through the process.
Which Template Should You Use?
Pick the template by the level and specialization you need. All five share the same skeleton, but each emphasizes the responsibilities, requirements, and stack that fit a specific kind of AI engineer role. Use this guide to choose.
5 Free AI Engineer Job Description Templates
Download all five as a single Word document or copy individual templates. Each follows the same structure: role overview, key responsibilities, requirements, nice-to-have, and compensation and how to apply, with an EEO statement included. Fill in the brackets before you post.
Template 1: General / Mid-Level AI Engineer
The universal mid-level version. Designs, builds, and deploys AI and ML features in production, integrating models and owning their reliability. Start here for most AI hires.
Template 2: Founding AI Engineer
For a first AI hire at a seed or Series A startup. High-ownership, end-to-end, ship-fast, with meaningful equity and direct founder collaboration. Built for zero-to-one work.
Template 3: Senior AI Engineer
For a senior hire who leads system design, owns production reliability, and mentors the team. Adds architecture authority, MLOps ownership, and technical direction.
Template 4: Applied AI / ML Engineer
For a product-focused engineer who ships models into the product. Adds experimentation, A/B testing, and ownership of product and business metrics across the model lifecycle.
Template 5: Generative AI / LLM Engineer
For building on large language models. Adds RAG pipelines, vector databases, prompt and orchestration work, and evaluation harnesses. The fastest-growing AI specialization.
What to Include in an AI Engineer JD
Every strong AI engineer job description shares the same core sections, with concrete duties rather than generic ones. The templates above are built around them, but it helps to see the difference between vague and specific wording.
| Weak bullet | Strong bullet |
|---|---|
| Work with AI | Build and deploy AI features in production |
| Know machine learning | Develop models with PyTorch or TensorFlow |
| Use LLMs | Design RAG pipelines and manage prompt orchestration |
| Deploy models | Own model monitoring, latency, and cost in production |
| Know cloud | Deploy on AWS, GCP, or Azure with Docker |
Specific, concrete duties attract candidates who understand the work and signal a serious employer. Keep the language neutral and inclusive too, since the EEOC prohibits job advertisements that show a preference based on protected characteristics. For a fuller framework, the SHRM guide to writing a job description covers the standard sections.
Skills and the AI Stack by Variation
Core AI engineering skills hold across the role, while each variation adds its own stack. List the real tools your team uses, and separate what is genuinely required from nice-to-have.
| Variation | Core stack | Adds |
|---|---|---|
| General / Mid | Python, PyTorch or TensorFlow, cloud, Docker | Production deployment |
| Founding | Full ML or LLM stack, cloud, end-to-end | Zero-to-one ownership |
| Senior | Expert Python, system design, MLOps | Architecture and leadership |
| Generative AI / LLM | LangChain or LlamaIndex, RAG, vector databases | Prompt and evaluation work |
The AI field moves fast, so name the real stack rather than every buzzword. A precise, current skills list signals a serious team and attracts stronger candidates. The professional community, including the Association for the Advancement of Artificial Intelligence, is a useful reference point for the field.
AI Engineer vs ML Engineer vs Data Scientist
The titles overlap and companies use them differently, but they emphasize different things. An AI engineer builds and deploys AI features; an ML engineer trains and tunes models; a data scientist analyzes data and derives insight.
| Role | Primary focus | Toolbox skews toward |
|---|---|---|
| AI Engineer | Build and deploy AI features | APIs, orchestration, production |
| ML Engineer | Train and tune models | Modeling, training infrastructure |
| Data Scientist | Analyze data and experiment | Statistics, analysis, insight |
At a small company, one person may cover all three. Do not over-index on the title: describe the actual work, integrating LLMs, training models, or analyzing data, and the stack involved, so candidates know which role they are applying for.
How to Write an AI Engineer Job Description
A strong AI engineer posting takes about fifteen minutes once you settle the level, the specialization, the responsibilities, and the compensation. Here is the process the templates are built around. If you are building out your team, the small business hiring guide covers the steps around the posting itself.
AI Engineer Pay and Outlook
AI engineer pay is high and varies widely by level, specialization, and location, and it is one of the fastest-growing compensation categories in tech. There is no dedicated federal occupation for the exact title, so the closest mapped occupation serves as a floor reference.
In practice, AI engineer pay often runs above that figure, especially for senior and generative AI roles and in high-cost metros, with equity a significant part of total compensation at startups. These are the most recent confirmed federal estimates for the mapped occupation.
| Level | Relative pay | Notes |
|---|---|---|
| Mid-level | Strong | Building and deploying features |
| Founding | Lower base, more equity | Early-stage, high ownership |
| Senior | Highest | Leadership and architecture |
| Generative AI / LLM | Premium | In-demand specialization |
For setting pay, treat the federal figure as a floor reference, research current market rates for the specific level and specialization, and offer a clear, competitive package, since AI talent is scarce and candidates compare offers closely.
Hiring an AI Engineer
AI engineers are among the hardest roles to fill, and strong candidates often have multiple offers. A precise posting, competitive pay, and a fast process are what win them. Here is how to do it well.
After You Hire: Onboarding an AI Engineer
AI engineer onboarding is about getting a high-value, high-access hire productive quickly while handling the setup carefully. The basics come first: the offer with the compensation and any equity spelled out, the I-9, tax forms, and state new-hire reporting, plus any confidentiality and intellectual-property agreements. Then comes role-specific onboarding: access to your code, cloud, data, and AI tooling, often with sensitive permissions, plus the context and standards they need to ship. For the broader flow, the new hire paperwork guide covers the documents and the training new employees guide covers running orientation with sign-offs.
The documents around the hire follow the usual sequence: the offer letter template for the terms and equity and the onboarding checklist template for the first weeks of access setup and ramp-up.
FirstHR fits this directly: e-signature for the offer, NDA, and IP agreements, document management for signed agreements and any certifications, training assignments with completion records for security and systems onboarding, an HRIS with an org chart placing the engineer on your team, and a self-service portal where they can see their information, which helps you get a scarce, expensive hire productive fast. Applicant tracking is coming soon to FirstHR; today the platform handles onboarding and document tracking once the candidate signs.
Frequently Asked Questions
What does an AI engineer do?
An AI engineer designs, builds, and deploys artificial intelligence systems in production. The core work includes developing or integrating machine-learning models, connecting large language models through APIs, building RAG and data pipelines, owning model performance and monitoring, and collaborating with product and engineering teams to ship AI features. The role increasingly leans toward applied, production work, building with LLMs, vector databases, and orchestration tools, rather than pure research. The exact scope varies by role and company. A founding AI engineer at a startup owns everything end to end; a senior engineer leads system design and mentors others; an applied engineer ties models to product metrics; and a generative AI engineer focuses on building on top of large language models. When hiring, describe the specific work and stack your role involves rather than a generic definition.
What should an AI engineer job description include?
A strong AI engineer job description includes a role overview, key responsibilities, requirements, nice-to-have skills, the compensation, and how to apply, written for the specific kind of AI engineer you need. Because the title spans founding, senior, applied, and generative AI roles, the most important things are to match the template to your stage and the work and to name the real stack, such as Python, PyTorch or TensorFlow, cloud and deployment tooling, and any LLM stack like RAG, vector databases, and orchestration frameworks. Be clear about the experience level and what the role will own. Include the compensation, since AI engineers expect a clear and competitive package and equity is often part of the draw at early-stage companies, plus an equal opportunity statement and a clear way to apply. The five templates here are each built for a specific kind of AI engineer so the posting matches the real role.
What skills should be in an AI engineer job description?
Required skills for most AI engineer roles include strong Python, machine-learning frameworks such as PyTorch or TensorFlow, cloud platforms like AWS, GCP, or Azure, containerization with Docker, and solid software-engineering fundamentals. Increasingly, roles also call for LLM-specific skills: retrieval-augmented generation (RAG), vector databases such as Pinecone or Weaviate, prompt design, and orchestration frameworks like LangChain or LlamaIndex. MLOps experience, including model monitoring and deployment pipelines, is a strong preferred qualification. The right mix depends on the role: a generative AI engineer needs the LLM stack, while a classical ML role leans more on modeling and training. List what is genuinely required separately from nice-to-have, and name the real tools your team uses rather than listing every popular technology, since experienced AI engineers can tell a thoughtful posting from a generic one.
How is an AI engineer different from an ML engineer or data scientist?
The titles overlap and companies use them differently, but there are general distinctions. An AI engineer most often builds and deploys AI features, integrating models, often LLMs, into applications and owning their reliability in production, so the toolbox skews toward APIs, orchestration, and engineering. An ML engineer more often trains and tunes models themselves, leaning toward modeling, training infrastructure, and the math behind it. A data scientist focuses on analysis, experimentation, and deriving insight from data, often producing models that engineers then productionize. At a small company, one person may cover all three. When you write the job description, do not over-index on the title: describe the actual work, whether it is integrating LLMs, training models, or analyzing data, and the stack involved, so candidates know which kind of role they are applying for.
How much does an AI engineer make?
AI engineer pay is high and varies widely by level, specialization, and location, and it is one of the fastest-growing compensation categories in tech. There is no dedicated federal occupation for the exact title; the closest mapped occupation is computer and information research scientists, whose median annual wage was $140,910 in May 2024 according to the Bureau of Labor Statistics. In practice, AI engineer salaries often run higher, especially for senior and generative AI roles in high-cost metros, and equity can be a significant part of total compensation at startups. Junior and mid-level engineers earn toward the lower end of the market, while senior and specialized generative AI engineers command the most. For setting pay, treat the federal figure as a floor reference, research current market rates for the specific level and specialization, and offer a clear, competitive package, since AI talent is scarce and candidates compare offers closely.
What is the projected job growth for AI engineers?
The closest federal occupation, computer and information research scientists, is projected by the Bureau of Labor Statistics to grow 20 percent from 2024 to 2034, much faster than the average for all occupations, with about 3,200 openings projected each year over the decade. That figure covers a broader research-oriented category, but the direction reflects what employers see on the ground: demand for AI and LLM skills has risen sharply, and AI engineers are among the hardest roles to fill, with strong candidates often holding multiple offers. For an employer, the practical takeaway is that this is a competitive, fast-moving market, so it pays to write a precise, current job description, set competitive compensation, and run an efficient hiring and onboarding process rather than a slow one that loses candidates.
What happens after I hire an AI engineer?
Once the candidate accepts, the hire moves into onboarding, and for a high-value, high-access engineer that means getting them productive quickly while handling the setup carefully. The first steps are the offer and paperwork: the offer letter with the compensation and any equity spelled out, the I-9, tax forms, and state new-hire reporting, plus any confidentiality and intellectual-property agreements. Then comes role-specific onboarding: access to your code, cloud, data, and AI tooling, often with sensitive permissions, plus the context and standards they need to ship. FirstHR fits this directly: e-signature for the offer, NDA, and IP agreements, document management for signed agreements and any certifications, training assignments with completion records for security and systems onboarding, an HRIS with an org chart placing the engineer on your team, and a self-service portal. Applicant tracking is coming soon to FirstHR; today the platform handles onboarding and document tracking once the candidate signs, which helps you get a scarce, expensive hire productive fast.