Looking for entry level AI jobs but don’t have a tech degree? Learn how regular people are breaking into artificial intelligence careers with practical skills.
So here’s something most people don’t know about working in artificial intelligence.
You don’t need to be a genius. You don’t need a fancy degree. And honestly, you don’t even need to love math.
I know that sounds strange because every article makes it seem like you need to be Einstein just to get started. But the truth is different now.
My friend Jessica graduated with a psychology degree. She worked at a clinic for two years before deciding it wasn’t for her. Last year, she landed one of those entry level AI jobs everyone talks about. She’s now testing chatbots for a healthcare company. No coding bootcamp. No computer science background. Just some online learning and a lot of persistence.
Her story isn’t rare anymore. Companies building artificial intelligence need all kinds of people—not just engineers who write code all day.
What These Jobs Look Like.
The roles companies need right now.
When you start looking for entry level AI jobs, you’ll notice something interesting. Not every position requires heavy technical skills.
Data annotation specialists spend their days labelling information. Maybe they’re marking objects in images. Or categorising text by topic. Or identifying sounds in audio files. The work trains AI systems to recognise patterns.
Testing specialists try breaking AI products before customers see them. They find bugs, document problems, and make sure systems work reliably. Good testers save companies from embarrassing launches.
Customer success people help users who bought AI tools but need guidance. You’re answering questions, solving problems, and gathering feedback about what works and what doesn’t.
Technical writers create documentation that normal people can understand. They test products, interview engineers, and write clear instructions.
Prompt specialists work with language models, figuring out how to phrase requests so AI gives useful responses. Some people in these roles came from writing or teaching backgrounds.
These entry level AI jobs exist because companies desperately need help. The field is growing faster than universities can train people
Why beginners get hired.
Something changed in the past few years. Companies realized waiting for the “perfect” candidate meant never hiring anyone.
The technology moves so fast that experience from even three years ago might not help much. Someone who learned current tools last month could be more valuable than someone using outdated methods.
Startups especially can’t afford to hire senior people for everything. They need people doing real work. And they’re often willing to train someone who seems smart and motivated.
There’s another thing too. Beginners ask questions that experienced people stopped asking. When you don’t know why something works a certain way, you question it. That catches problems everyone else overlooks.
Data Work: Where Most People Start.
What your day looks like.
Let me describe what data annotation involves, because it sounds more boring than it is.
You log into a platform showing images, text, or audio. Your job is reviewing each item and adding accurate labels. The specifics depend on what the company builds.
Say you’re working for a medical imaging company. You look at X-rays and mark areas showing potential problems. “This looks like a fracture. This one’s normal. This shows some abnormality.”
First few hours go smoothly. Clear examples, easy choices. Then you hit tricky cases. Blurry images. Things you haven’t seen before. Situations where the right answer isn’t obvious.
You make judgment calls. Document the difficult cases. Sometimes ask supervisors for guidance. Over time, you build expertise that makes you more valuable.
Here’s what people miss about these entry level AI jobs in data—you’re teaching machines how to learn. They can’t figure things out alone. They need examples. Thousands of good examples. That’s what you provide.
Different types of data work.
The work varies depending on the industry.
Self-driving car companies need people who identify objects in street scenes. Is that a pedestrian or a mailbox? Is the light red or yellow? These distinctions matter because AI learns from your choices.
Translation tools need people comfortable with multiple languages. You check whether automated translations captured the right meaning and tone.
Financial firms want people who can categorize transactions correctly. You look at purchasing patterns and decide what seems normal versus suspicious.
E-commerce companies need product categorization. You’re tagging items, describing them consistently, making sure everything is organized logically.
Building expertise in a specific domain makes you increasingly valuable over time.
The honest reality
I won’t sugarcoat this. Some of these entry level AI jobs are contract positions ending after a few months. Some don’t pay amazingly when you start. Parts of the work can feel repetitive.
But consider what you gain. You’re learning how AI systems work while getting paid. You’re building experience with industry tools. You’re developing specialized knowledge.
I know someone who started doing basic image labeling for $18 an hour. She noticed process inefficiencies and suggested improvements. Within a year, she was training new people. Two years in, she moved into a product role helping decide what features to build.
The job was a door, not a dead end.
Testing Roles: Breaking Things On Purpose.
What this involves
Testing AI systems needs a suspicious mindset. You assume things will break and prove yourself right.
Imagine testing a customer service chatbot. Your job is to ask every question you can think of. What happens with spelling errors? What about profanity? Questions the bot wasn’t designed for?
You document everything that fails. What you did, what happened, how serious it seems, how to reproduce it. Developers use your reports to fix problems before real customers encounter them.
Many entry level AI jobs in testing don’t require coding skills. They need attention to detail and systematic thinking.
Why this matters.
One serious bug can destroy a company’s reputation overnight.
Remember when that chatbot gave dangerous medical advice? Or when a hiring algorithm discriminated against qualified people? Those disasters happened because testing wasn’t thorough enough.
When you catch critical problems before launch, you’re preventing real damage. Companies value people who protect them from expensive mistakes.
The skills transfer everywhere, too. Understanding user behaviour helps in product management. Documenting issues clearly matters in any role. Thinking through edge cases is useful wherever you go.
Communication Roles
What you’d be doing
Some entry level AI jobs focus on helping people understand and use products.
Customer success puts you in contact with users. Someone bought an AI tool, tried using it, got confused. They reach out for help. You figure out what went wrong and help them fix it.
This needs patience. People contact support when frustrated. Your job is staying calm, listening carefully, and solving problems without making them feel dumb.
Technical writing means creating documentation that makes sense. You test products, talk to engineers, and write guides that help users figure things out independently.
Training roles involve teaching customers how to use features effectively. You might run webinars, create videos, or work one-on-one with new users.
Skills that help.
You don’t need coding for most communication-focused entry level AI jobs. You need different abilities.
Can you stay calm with frustrated people? If you worked retail or food service, you already developed this skill.
Can you explain complicated things simply? That’s more valuable than you’d think.
Are you patient and empathetic? The best support people genuinely care whether customers succeed.
Can you write clearly? Technical writing requires precision and making complex topics accessible.
Skills Worth Learning
Technical foundations
Get comfortable with spreadsheets. Excel or Google Sheets. Can you organize messy data? Create pivot tables? Use formulas? Much of data work starts here.
Learn basic statistics. Not advanced math. Just concepts like averages, percentages, distributions. Khan Academy teaches this free.
Try SQL if you want an advantage. It’s how you ask databases questions. Learning basic queries takes about a month and makes you more hireable.
Python appears in many descriptions for entry level AI jobs. Some need it, others don’t. Try a beginner tutorial. If you enjoy it, keep learning. If you hate it, focus elsewhere.
Understanding basic AI concepts helps during interviews. Know terms like machine learning, training data, model accuracy.
Soft skills that matter.
Technical abilities get interviews. These other skills determine success.
Can you learn independently? The field changes constantly. Self-directed learners stay relevant.
Do you ask good questions? Curiosity helps you spot problems others miss.
Can you handle uncertainty? Requirements change. Problems lack obvious solutions. You need to adapt.
Are you reliable? Showing up on time, meeting deadlines, tracking details. Employers value dependability highly.
Can you work with different people? Projects involve diverse teams. Being collaborative matters.
Breaking In: What Works
The application strategy.
People who successfully land entry level AI jobs follow similar patterns.
They learn foundational skills first. Not everything. Just enough to have informed conversations and show basic competence.
Then they apply widely. Not just to famous companies. Those get thousands of applications. They apply to startups, consulting firms, and traditional companies building AI capabilities.
They apply to 50, 80, sometimes 100+ positions. They get rejected constantly. Most applications vanish. A handful lead to calls. Eventually something works.
The people who succeed keep going after rejections.
Improving your odds
Having a simple portfolio helps when applying for entry level AI jobs. Not a fancy website. Just something showing you can apply knowledge. Maybe you cleaned a dataset and visualized results. Or tested an open-source tool and documented findings.
Tailor your resume to each position. Read descriptions carefully. Highlight matching experiences. Use similar language.
Write brief, genuine cover letters for smaller companies. Just a few paragraphs explaining why their work interests you.
Follow up appropriately. If you haven’t heard back in two weeks, one polite email is reasonable.
Talking to people
Attend local tech meetups or AI events. Show up, listen, maybe ask questions. Go consistently and you start recognizing faces.
Join online communities focused on AI. Read what people post. Contribute when you have something useful.
Message people on LinkedIn who have jobs you want. Keep it brief. Don’t ask for jobs. Ask about their path and advice.
Most messages get ignored. Some respond. Those conversations teach you things and occasionally lead somewhere.
Questions People Ask
Does my degree matter?
What helps more: proving you can do the work. Projects showing competence. Courses completed. Evidence of commitment.
People with unrelated degrees work in entry level AI jobs all the time. English majors, business graduates, even dropouts who taught themselves skills.
How long before I’m ready?
Depends on your starting point.
Someone comfortable with computers aiming for data work: 1-3 months of focused learning.
Someone new to tech wanting testing roles: 3-6 months.
More technical positions: 6-12 months of consistent study.
But you’ll never feel completely ready. Start applying and see what happens.
Can I work remotely?
Many entry level AI jobs offer remote options now. Data annotation often allows it. Testing can be remote. Support varies by company.
Listings usually specify requirements. If unclear, ask during interviews.
What comes after?
Paths branch everywhere after landing entry level AI jobs.
Some go deeper technically, becoming engineers or data scientists. Others move toward management, coordinating projects and teams. Some shift into specialized areas like ethics or policy.
Your first role isn’t permanent. It teaches you what you enjoy and where to focus.
Real Talk
Breaking in takes work. You’ll learn skills, face rejection, question whether it’s worth effort.
But it’s not impossible. Entry level AI jobs exist because companies need help and will train motivated people. The field grows fast enough that opportunities keep appearing.
If this interests you, stop waiting. Pick one area—data, testing, or communication. Learn core skills. Build something small. Start applying.
Talk to people doing the work. Ask about real experiences. Those conversations teach more than articles.
When rejection comes, keep applying. Most quit too early. The ones who succeed are just more persistent.
You might be one of them. Only way to find out is starting.
