How to Start a Career in Artificial Intelligence

Starting a career in AI doesn’t mean you need to be a math genius or get accepted into MIT. It also doesn’t mean you need to drop everything and become the next OpenAI co-founder. Like with any career, the path into artificial intelligence is more flexible than most people think. Yes, it can be challenging, but it’s also wide open, especially if you know how to position yourself. I’ll talk how to break into AI, and include a real-world experience from someone who did exactly that, starting out as an intern in India, navigating the move to the US, and eventually landing a spot on one of the core Foundation Model teams at a major tech company.

What do you want, and where do you fit?

When people say they work “in AI,” it can mean anything from designing large language models to writing glue code for data cleaning scripts. The field isn’t a monolith. On any given machine learning team, there are usually researchers working on cutting-edge algorithms, engineers building product features on top of those models, machine learning specialists training and fine-tuning systems, and infrastructure teams building the tooling and systems to keep everything running smoothly. The first real step is to figure out where you want to land in that ecosystem. Some people are fascinated by the model-building process itself, others want to work closer to product or application. And not every role requires a PhD or academic background, but every role does require some depth. Whether that’s fluency in Python, an understanding of linear algebra, or the ability to translate business problems into solvable technical challenges, you need to show you can contribute something real. And you’ll also want to be sure this is actually the career you want, because “working in AI” doesn’t mean one thing. AI is showing up in every imaginable industry. You could be fine-tuning models for cancer detection, optimizing financial algorithms, or even building NSFW, 18+ AI chatbots. The range is wide, and so is the culture. Figure out what kind of problems you actually want to wake up and solve.

Once you’re on the job, things get more structured, but no less complex. A typical day for someone working in AI might start with research. Reading papers, analyzing model outputs, trying to understand what’s broken and why. From there, the day often includes collaboration, checking in with other engineers, product managers, and sometimes clients to make sure what you’re building aligns with what’s needed. Finally, there’s the actual building. Writing code. Tuning models. Running experiments. Shipping something that doesn’t break. In reality, these phases bleed into each other. You might spend one week deep in technical weeds and the next explaining your results to a skeptical stakeholder. It’s not always glamorous.

You’re still interested? Ok. There are two major paths into AI. One is academic, the other is practical. Neither is universally better, it depends on where you’re starting from and where you want to go.

The academic route usually means pursuing a master’s or PhD in computer science or AI-related fields. For many, this isn’t just about the education itself but about proximity. Graduate programs introduce you to people, professors, and projects that can open doors. And if you’re an international student, a university degree often doubles as a visa strategy, providing legal footing and time to integrate into a new country’s job market. On the other hand, there’s the practical path. Learning by building. If you’re skipping school, your portfolio becomes your credibility. That might mean training a basic model on open-source data, launching a side project, publishing on GitHub, or documenting your learning publicly. It’s a longer road in some ways, but it forces you to get your hands dirty early. And employers often care more about whether you can build something functional than whether you can pass an exam.

Internships and networking still matter

Internships are underrated. For many people, especially those switching careers or coming in from abroad, internships are the fastest way to cut the line. It might feel awkward to go back to being “the intern” if you already have a few years of work under your belt, but that title doesn’t matter nearly as much as the access it gives you. One engineer from India started their AI career by interning for a U.S. company after previously working for its India office. They didn’t wait for a job listing to pop up so they reached out directly to managers who had worked with them before. That familiarity helped them bypass the resume pile and go straight to interviews. Once inside, they went further. They built internal tools and pitched them to other teams, showing initiative and solving real problems. When it came time to argue for a full-time position, those internal projects were what sealed the deal. That’s the playbook. Don’t treat internships like academic exercises. Treat them like a tryout for a real job.

The biggest unlock in any tech career, especially in AI, is proximity to opportunity. Being in the right communities, knowing the right people, and showing up in the right conversations can do more than a stack of certificates ever will. For people not living in major tech hubs, online communities become vital. That might mean engaging in GitHub issues, joining Discord servers, hopping into Twitter spaces, or contributing to open-source AI tools.

If you’re in a city like San Francisco or New York, things can move even faster. You don’t need a grad school network, you just need to show up where things are happening. Industry events, meetups, low-key hackathons. These are places where introductions happen and opportunities start. The key is not to walk in begging for a job. The goal is to become visible, consistent, and easy to trust.

Understanding the bias in hiring

You don’t need a master’s degree to land an AI job, but it would be dishonest to pretend there isn’t some bias in play. Many teams, especially at larger companies, still default to filtering candidates by education level. This isn’t impossible to overcome, but it does mean your portfolio and public work need to be sharp. If you don’t have academic credentials, your best move is to out-build, out-document, and out-share. People want to see that you can solve real problems, write clean code, and understand trade-offs. Whether that shows up in the form of a Kaggle project, an open-source contribution, or a smart LinkedIn post, the key is to make your skills undeniable. Since most AI opportunities are either in USA or China, does that mean you’ll have to move? Not always. But for some people, it helps. The engineer from the beginning of the story described how she felt limited working in their home country because the core business decisions were being made elsewhere. She felt like a contributor, not a decision-maker. So she moved. Not for lifestyle reasons, but for leverage. In her case, the move to the U.S. put them closer to the strategic side of their work. She had access to teams, conversations, and opportunities that simply weren’t available back home in India. Of course, that won’t be true for everyone. But if you’re hitting a ceiling in your current location, whether due to policy, company structure, or market immaturity, relocation might be worth considering.

Is getting in easier than staying in?

Breaking into AI doesn’t follow a template. There’s no one-size-fits-all checklist, no golden certification that unlocks it all. The path is more fluid, more personal, and more random than most blog posts will admit. But there are patterns that show up again and again. The people who make it in tend to move early, build in public, and ask for feedback. They don’t just chase titles but problems they want to solve. They work in the open. They’re visible. They document their progress. And most importantly, they’re willing to be bad at something long enough to get good.

AI is still young. It’s moving fast. But it’s not full yet. The field is full of self-taught engineers, career switchers, people who used to be designers, biologists, musicians, even med school dropouts. If you’re motivated, adaptable, and genuinely curious, there’s space for you too. You just have to start.