For aspiring AI/ML engineers, GitHub projects alone won't get you a job.
A few years ago, a couple of good projects could be enough to get your foot in the door. You could show that you knew how to work with data, train a model, and build something beyond a course exercise.
Today, breaking into the industry is much harder.
After waves of layoffs, you may be applying alongside candidates with years of industry experience. They have projects too. They also have experience working on systems inside a business, dealing with changing requirements, and delivering work other people depend on.
A good repository helps you demonstrate what you've learned. It doesn't remove that experience gap.
Keep the portfolio. Widen the search.
This isn't a reason to stop building projects. They give you something concrete to discuss and a way to develop your skills while looking for work.
But you can put months into a portfolio and still struggle to secure an interview. If your entire plan is to keep adding repositories and applying for AI/ML engineering roles, you're relying on one route into a competitive industry.
Look at other ways to get your first relevant experience. You can work toward an AI/ML role without making that exact title a condition of your first job.
Four entry points worth considering
1. Networking
Talk to people already doing the work. Former classmates, colleagues, local communities, and professional events can all lead to useful conversations.
Ask how they entered the field and what their teams actually need. You may learn about roles you weren't searching for or get a chance to discuss your work with someone who would never have found your GitHub profile.
A conversation won't guarantee an interview. It gives you another way to be considered beyond submitting an application and waiting.
2. Analyst roles
An analyst role can put you close to real data, business questions, and the people making decisions from the results.
You get experience with problems that arrive without a clean dataset or a prepared problem statement. You learn how the business works and how to communicate what the data means.
That experience can support a later move toward data science or ML. Look for roles where you can develop relevant skills and contribute, rather than treating the title as a detour.
3. Internships, if you're eligible
Internships can provide a first opportunity to work with a team on an actual business problem.
Eligibility varies, so check the requirements. If you qualify, include them in your search. The experience and relationships you develop may be more useful than adding another independent project to your profile.
4. Other entry-level opportunities
Consider roles involving reporting, data quality, data operations, or junior software work where your current skills fit.
Read what the job involves. Look for opportunities to work with data, write code, solve problems, and learn from experienced colleagues. Those responsibilities matter more than whether the title already says AI Engineer.
Your first role can be a starting point
Moving into AI/ML may take more than a strong GitHub portfolio. It may take an adjacent role, a useful professional connection, or an internship that gives you the experience employers keep asking for.
Keep developing your skills and building work you're proud to show. Give yourself more than one way into the industry.
Your first job doesn't have to be the role you ultimately want. It can be the experience that helps you get there.
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