Tips for Landing your First AI Role
If you're looking to work in the AI world, here are some tips to get you started
The AI sector is one of the hottest and fastest-growing industries today. With the explosion of new products, startups, and research breakthroughs, it’s no surprise that more and more people want to work in this field. From data analysts to AI engineers and project managers, the demand for talent is massive.
And while that’s true, getting a job as a beginner can feel increasingly frustrating. Still, in a world full of opportunity and constant change, the real question is: how can you position yourself to maximize your chances of landing that first role? After all, this is an industry where roles evolve fast, and the same technology that creates new jobs might one day automate parts of them. That tension is what makes building a career in AI extremely challenging and exciting.
After interviewing more than 500 candidates and mentoring over 15 people who successfully entered the job market in the past two years, I’ve identified a few patterns. The people who break in usually follow certain habits and approaches that make all the difference.
In the next sections, I’ll share what I’ve seen work best (practical strategies, mindset shifts, and portfolio tips) to help you get your foot in the door and start a meaningful career in AI.
Join In-Person Events
One of the hardest parts of job hunting, especially when you have little or no experience, is breaking through the CV tsunami. Hundreds of applications flood every open position, and yours can easily get lost in the noise.
The most effective way to bypass that first filter is, unfortunately, through referrals and personal connections. Many companies fill roles internally or through trusted networks long before posting anything publicly. Being part of that inner circle gives you access to opportunities that never reach LinkedIn or job boards.
To do that, you’ll need to step outside your comfort zone and network intentionally. Attend AI meetups, tech talks, conferences, or local hackathons. Talk to speakers, ask questions, and connect with people who work in the roles you aspire to. It may feel awkward at first, but over time, people begin to remember you, along with your curiosity, your questions, your energy.
But hey - be genuinely interested in people. Before attending an event, research who’s going to be there. Learn about their work, their company, or the projects they’ve contributed to. Ask thoughtful questions that show you’ve done your homework. If you’re only networking to “tick a box,” it will come across as forced and transactional.
Real networking is about building authentic curiosity and finding ways to learn from others. When you focus on genuine conversations, trust follows, and so do job opportunities.
Understand what you want to do
It’s completely normal to feel lost at the beginning. The AI field is broad - ranging from data analysis to machine learning engineering, from research to project management - and figuring out where you fit can be overwhelming. But the more specific you are about what you want, the easier it becomes for others to help you and for recruiters to see where you belong.
Start by narrowing your options and asking yourself a few key questions:
Do you want to work on something technical or non-technical (coding vs. strategy, coordination, or management)?
Are you more drawn to data science (predictive modeling, experimentation) or data analysis (insight generation, reporting, visualization)?
Are you open to data engineering or prefer staying closer to the business side of AI projects?
You don’t need to have all the answers right away. But identifying what you don’t want to do is often just as useful as knowing what you do. This kind of clarity helps tailor your CV, and communicate your goals more effectively when networking or applying for jobs.
People who know what they’re aiming for come across as more confident and intentional.
Apply, even if you don’t match all the criteria
This is one of the biggest mistakes beginners make: they don’t apply because they think they lack experience with some of the tools or technologies listed in the job description.
In reality, for most entry-level roles, those long lists of technologies are more of a wishlist than a strict requirement. Companies often describe their “ideal” candidate, not the realistic one they’ll hire. And yes - some job posts can be downright absurd, asking for expertise in a dozen machine learning libraries for a position that’s essentially focused on Excel, Power BI, or basic data analysis. Or asking for 10 years experience in a framework that was born 5 years ago.
If you meet even 60–70% of the criteria, apply. The learning curve in your first job will always be steep, and most companies know that. They’re looking for people who can learn fast, communicate clearly, and show initiative - not for someone who already knows everything.
You’ll rarely regret applying, but you’ll often regret not doing it.
Showcase your work
A project portfolio is one of the most powerful ways to showcase your skills before you even step into an interview. It gives recruiters and hiring managers a clear sense of what you can do, not just what you say you can do.
But having a portfolio isn’t just about uploading random notebooks or scripts to GitHub. Think of it as your professional story told through projects. Each project should highlight something specific, such as your problem-solving skills, your technical depth, or your ability to connect data work to real business value.
When organizing your portfolio (on GitHub, a personal website, or platforms like Notion or Medium), think about the impression it gives to someone seeing it for the first time. Use clear README files, short project summaries, and visuals that make your work easy to follow.
Your projects don’t need to be massive or perfect. A well-documented, small, real-world example often speaks louder than a complex, unfinished one.
Volunteer
Few things open doors in your career like getting involved with volunteering associations or community projects in the data and AI space. It’s one of the most effective ways to build genuine relationships, gain visibility, and develop hands-on experience - all at once.
That’s actually how I met my co-founders at DareData. By volunteering in data-related initiatives, I connected with people who shared the same curiosity and drive to build real things. Over time, those collaborations turned into friendships, and later, into a company.
Volunteering gives you more than just connections. It shows that you care about contributing to something larger than yourself. Whether it’s supporting a non-profit with data analysis, mentoring students, or helping organize community events, it allows others to see how you work, think, and collaborate.
If you want to stand out, don’t just join communities, contribute to them. That’s how people remember you, and how opportunities start to find you instead of the other way around.
Breaking into AI takes consistency more than perfection. Once you’ve clarified your goals and started building connections, focus on taking small, visible actions that compound over time.
To wrap up this blog post, here are some practical next steps you can take.
Create a GitHub, Notion page, or personal website to showcase your projects. Keep it simple but well-organized. This becomes your digital portfolio - something you can share with recruiters or new contacts right away.
Start uploading projects and craft a story behind them. Explain the “why” behind each one, what you learned, and what impact it could have. Storytelling turns technical work into something memorable.
Search for in-person and online events on Meetup or Luma. Attend AI and data-related talks, workshops, and hackathons to meet people and learn from real practitioners.
Look for volunteer organizations in your area focused on AI and data. In Portugal, examples include Data Science for Social Good Portugal, the Lisbon Data Science Academy, and the Data Science Portuguese Association (DSPT). They’re great entry points to gain experience and connect with the community.
Apply to jobs even if you don’t meet every requirement. Recruiters know junior candidates are still learning. Curiosity and initiative often matter more than ticking every box.
Tailor your CV for the roles you actually want to do. Highlight relevant coursework, side projects, and tools related to your target role instead of listing everything you’ve ever done.
Document your learning publicly. Write short posts on LinkedIn or Medium about your process, lessons, or insights from projects. It builds your credibility and helps others find you.
Join online AI and data communities on Slack, Discord, or Reddit. Participate in discussions, ask questions, and offer help when you can.
Getting your first AI job isn’t about luck or a perfect resume. The people who move fastest are those who show up - online, in person, and in the work they share. Start small, stay engaged, and let your projects and connections do the talking!
Let me know any other tips you’ve found relevant for your job search in the comments. They may be interesting for other people reading this!