If you’re applying for a role in artificial intelligence (AI), data science, or machine learning (ML), your resume needs to do more than just tick boxes. It should clearly show your technical ability, problem-solving mindset, and interest in the field — all in a format that’s easy for hiring managers (and applicant tracking systems) to understand.
Here’s how to craft a strong, focused AI resume — whether you're a recent graduate, early-career developer, or making a career switch into tech.
What to Include in an AI Resume
A good AI resume includes all the basics — but the way you present each section can make a big difference. Here are the key sections you’ll want to include:
Professional Summary (Optional but Helpful)
Use this to position yourself. Focus on your current level, relevant interests, and what kind of roles you're targeting.
Example:
Graduate data analyst with strong Python and SQL skills, passionate about ethical AI and machine learning applications in healthcare. Seeking a junior data science role where I can apply data modelling and visualisation skills to real-world challenges.
Tip: Focus on clarity, not cleverness. If you’re not sure whether to include a summary, it’s okay to leave it out.
Technical Skills
Tailor this section to what’s relevant—don’t list every tool you've ever touched.
Suggested format:
Group your skills into categories:
- Languages: Python, R, SQL
- Frameworks: TensorFlow, PyTorch, scikit-learn
- Tools: Jupyter, Git, VS Code
- Data: Pandas, NumPy, Power BI, Tableau
- Cloud & DevOps: AWS, Docker, GitHub Actions (only list if you've used them)
✅ Keep it short and skimmable
✅ Prioritise tools mentioned in the job ad
🚫 Don’t include soft skills here (save them for your experience section)
Work Experience
Even if you haven’t had a formal AI job, show how your experience applies.
Include:
- Job title, company, and dates
- A short description of your responsibilities
- 2–4 bullet points of achievements or relevant tasks
Highlight:
- Data wrangling or analysis in non-AI roles
- Working with large datasets, Excel reports, or business insights
- Internships, freelance work, tutoring, or hackathons
Example:
Data Analyst Intern – GreenTech Solutions (Feb 2024 – Jul 2024)
- Built Python scripts to clean and merge sensor data from smart energy meters
- Used seaborn and matplotlib to create monthly performance dashboards
- Identified a 12% drop in data accuracy due to sensor drift, contributing to a team-wide solution
Projects
Projects are your secret weapon—especially if you’re still building experience. Choose 2–4 projects that show how you apply your skills.
Include:
- A short title and one-liner description
- Key tools used
- A GitHub link or portfolio link
Example:
Spam Detection Model (2023)
Built a logistic regression classifier in Python using scikit-learn to identify spam messages (95% accuracy). View on GitHub
Tip: Focus on 2–4 quality projects. Explain the "why", not just the tech.
Education & Certifications
List your highest and most relevant qualification first.
Include:
- Degree, major, institution, and year completed (or expected)
- Any AI, ML, or data coursework
- Certifications like:
- Google Data Analytics Certificate
- AWS Machine Learning Specialty
- fast.ai, DeepLearning.AI, etc.
How to Showcase Technical Skills (The Right Way)
Rather than just listing “Python” under your skills, show where and how you’ve used it. Tailor your resume to match the specific job ad:
- If a job mentions “model evaluation and tuning”, include a line like:
“Tuned hyperparameters using GridSearchCV to improve model F1 score by 8%.” - If it mentions “version control”, include:
“Used Git and GitHub to manage team collaboration on Python-based data pipeline.”
Always match your language to the role — without copying the job description word-for-word.
Linking to Portfolios, GitHub, or Notebooks
Include links to your best work — ideally right at the top of your resume or in your project section.
Here’s how to do it well:
- Link directly to specific repositories or notebooks, not just your GitHub profile.
- Add short descriptions to help context.
e.g. “See project: Loan Default Predictor” - Make sure the repo is tidy: include a README.md, clean code, and comments.
If you have a personal website or online portfolio, include that too.
Formatting, ATS Optimisation, and Australian Resume Tips
Hiring software — known as applicant tracking systems (ATS) — often scans your resume before a human sees it. Keep your formatting clean and machine-readable:
- Use standard section headings (e.g. “Skills”, “Projects”, “Education”)
- Avoid tables or text boxes (they can confuse ATS)
- Save your file as a PDF unless told otherwise
Australian resume conventions:
- You don’t need to include a photo, age, or marital status
- Keep it to 1–2 pages max
- Use Australian spelling (e.g. “optimise”, not “optimize”)
Common Mistakes to Avoid
- Too much jargon: Avoid long technical lists with no explanation.
- Generic descriptions: Be specific about your work and results.
- Overloading your resume: Focus on quality, not quantity — include your best 2–4 projects, not every tutorial you’ve followed.
- Missing links or broken URLs: Double check everything works.
- Typos and formatting errors: These are small things that make a big impression — use a spellcheck and ask a friend to review it.
Ready to Apply?
Don’t be disheartened if you don’t hear back right away. AI and data roles often attract a high volume of applicants, and it can take a few tries to land the right fit. Every tailored application builds your skills and gets you closer.
Explore AI, data, and machine learning roles across Australia at AI-Jobs.com.au — and take the next step in your tech career.