AI Engineer job description.
An AI engineer designs, builds, and deploys machine learning models and AI-powered systems that solve business problems. They work across the model lifecycle, from data preparation and training to integration into production applications, collaborating closely with data scientists and software engineers.
The AI Engineer job description · $29
The full editable .docx — role summary, 8 worked responsibilities, qualifications, and skills, formatted for your letterhead. Delivered to your inbox within 24 hours — usually instantly.
Role: AI Engineer Reports to: Engineering Manager or Head of AI
An AI engineer designs, builds, and deploys machine learning models and AI-powered systems that solve business problems. They work across the model lifecycle, from data preparation and training to integration into production applications, collaborating closely with data scientists and software engineers.
- Design, train, and evaluate machine learning and AI models
- Build data pipelines to prepare and process training data
- Deploy models into production systems and monitor performance
What's inside the document.
One-paragraph plain-English explanation of the role's outcome and scope.
8 responsibilities phrased the way the work is actually done.
4 qualifications a candidate must have to perform on day 30.
3 qualifications that would make a candidate excellent in year two.
6 skill chips you can copy directly into your ATS.
Engineering Manager or Head of AI
A complete document set.
- Word document (.docx) — fully editable
- PDF — signature-ready
- Google Docs — one-click copy to your Drive
- 12 months of updates to this document
- Commercial-use licence for internal and client work
The work, not the title.
- Design, train, and evaluate machine learning and AI models
- Build data pipelines to prepare and process training data
- Deploy models into production systems and monitor performance
- Optimize models for accuracy, latency, and cost efficiency
- Collaborate with data scientists and software engineers on integration
- Research and evaluate new AI techniques and tools
- Document model architecture, assumptions, and limitations
- Ensure responsible AI practices around bias, fairness, and privacy
Required — and what would make a candidate excellent.
- Bachelor's or master's degree in computer science, AI, or related field
- 3+ years of experience building and deploying machine learning models
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar)
- Strong understanding of algorithms, statistics, and data structures
- Experience with large language models and generative AI
- Experience deploying models on cloud platforms (AWS, Azure, GCP)
- Publications or contributions to open-source AI projects
Eight steps from download to publish.
- 01Open the AI Engineer job description in Word or your one-click Google Docs copy.
- 02Replace placeholders for company name, reporting line, and location with your specifics.
- 03Tighten the summary to one paragraph that names the team's outcome, not just the role.
- 04Edit the responsibilities to match the actual scope of the seat — aim for 6 to 8 items, not 12.
- 05Separate required qualifications from preferred. Required is what a candidate must have to do the work on day 30; preferred is what would make them excellent in year two.
- 06Add salary range guidance using BLS, Payscale, or your own band data — do not copy generic figures.
- 07Have the hiring manager and one peer read it. Cut anything that wouldn't survive a candidate question.
- 08Publish to your ATS, intranet, and external careers page.
The right document at the right moment.
Use this AI Engineer job description any time you are opening or reopening a seat at this level. The senior band sets the calibration — copy the document, tighten it to your specific scope, and circulate to the hiring panel before the first interview.
The reporting line (Engineering Manager or Head of AI) and skills list are starting points. Override either if your org structure or stack differs from the norm — the template is a draft, not a contract.
Honest answers before you download.
- How is an AI engineer different from a data scientist?
- An AI engineer focuses more on building and productionizing models and systems, while a data scientist often focuses on analysis, experimentation, and model research; many organizations blend the two roles.
- What experience level is typical for this role?
- Most AI engineer roles are mid-to-senior level given the technical depth required, though some organizations hire junior engineers into supporting positions on AI teams.
Other documents in this neighbourhood.
Data Scientist
A Data Scientist analyzes structured and unstructured data to uncover patterns, build predictive models, and inform business decisions.
Software Engineer
A Software Engineer designs, builds, and maintains software systems.
Data Engineer
A data engineer builds and maintains the pipelines and infrastructure that collect, store, and process data at scale.
This AI Engineer job description is a professionally drafted starting point for your hiring process and is not legal advice. Hiring practice varies by jurisdiction (e.g. pay-transparency laws differ across US states and AU jurisdictions). Adapt this document for your specific location and have employment counsel review any clauses you add before publishing. Salary varies by region, employer type, and experience. Reference BLS or current industry surveys for ranges. Full disclaimer.