Artificial intelligence is no longer just a futuristic concept, but an integral part of our lives. It is already used in all areas of human activity - in marketing, sales, content-making, healthcare, finance, transportation, entertainment, etc., and continues to change many other industries. And this tendency shows no signs of stopping.
With companies looking to build smarter products and save time through automation, jobs in AI are growing fast. The World Economic Forum states AI and big data are the top first among the fastest-growing skills now, with a projected net increase in demand of 87% by 2030. This forecast is driven by technological advancements, with 86% of employers expecting AI and information processing to transform their businesses.
So, if you’re considering building a career in this area, there are plenty of positions expected in the future and many paths to get started. The challenge is to understand which of them to pursue, how to get started, and how to stand out in the competition as it will still be tough despite the growing demand for specialists.
In this blog, we will walk you through the essentials: from learning the core AI concepts and building a solid technical foundation to gaining hands-on experience, creating a portfolio, and landing your first role. Here is your step-by-step roadmap:
Step 1. Learn AI and Machine Learning Concepts
Start by exploring the main terms in AI, like machine learning, neural networks, and deep learning:
Machine Learning (ML): is the heart of AI - the process enabling computers to analyze data and find patterns to improve their algorithms. It encompasses supervised learning (e.g., predicting outcomes with labeled data), unsupervised learning (e.g., finding patterns in unlabeled data), and reinforcement learning (e.g., learning through rewards and penalties). It is important to understand models such as linear regression (for continuous predictions), logistic regression (for classification), decision trees, clustering (e.g., k-means), and anomaly detection.
Beyond specific models, it is even more important to understand the basic concepts behind how and why ML works, such as:
Bias/Variance Tradeoff: Balancing model complexity to avoid underfitting or overfitting.
Loss Functions: Metrics (e.g., mean squared error) that quantify model performance.
Regularization: Techniques (e.g., L1/L2 regularization) to prevent overfitting.
Optimization Algorithms: Methods like gradient descent to minimize errors.
Error Analysis: Diagnosing model weaknesses to improve performance.
Deep Learning: is a subset of ML that uses neural networks with multiple layers to model complex patterns. It mimics human brain processes and excels in tasks like image and speech recognition. It is important to understand such basics as neural networks (e.g., neurons, layers, activation functions), convolutional neural networks (CNNs) for images, recurrent neural networks (RNNs) for sequences, and transformers for advanced applications like language models and the intricacies of their tuning for optimizing performance (such as hyperparameter tuning).
Natural Language Processing (NLP): enables computers to understand and generate human language, powering applications like chatbots, sentiment analysis, and machine translation. Key concepts include tokenization, word embeddings (e.g., Word2Vec, GloVe), and large language models (e.g., GPT). It is also important to understand techniques for text classification, sentiment analysis, and named entity recognition.
Computer Vision: allows machines to interpret visual data such as images and videos, for tasks like object detection, image classification, and generative AI (e.g., creating images). Basics include convolutional neural networks (CNNs), image preprocessing, and models like YOLO for object detection. It is also vital to explore generative adversarial networks (GANs) for creating synthetic images.
You don’t need to master everything at once. Start with one area that excites you - if you enjoy words, try NLP or chatbots. If you like visuals, explore AI in games or images. Keep in mind that each AI domain leads to distinct roles that have unique skill requirements. For instance, ML and predictive AI suit data scientists and machine learning engineers, deep learning and computer vision align with computer vision engineers, and NLP is ideal for NLP engineers.
Top Careers in AI include:
AI Engineer: builds AI systems for real-world problems
Machine Learning Engineer: Designs and deploys ML models
Data Scientist: Analyzes data and develops predictive models
AI Research Scientist: Conducts theoretical research to advance AI models
NLP Engineer: Builds systems that work with human language
Computer Vision Specialist: Develops AI systems for image and video analysis
Software Engineer: Develops apps that integrate AI
AI Product Manager: Bridges technical teams with business goals.
Step 2. Build a Strong Tech Foundation
Having explored AI’s exciting concepts and applications, the next step is building a strong foundation with technical skills that power these technologies. It means that to truly succeed in AI and open the doors to advanced roles like ML engineer or AI research scientist, you need a solid grasp of math and programming basics.
Mathematics: While you don’t need advanced math to start in AI, as modern tools do a lot of the hard work, still, knowing the basics makes it easier to understand what’s going on. If you go deeper into areas like deep learning, math matters more.
Focus on:
Linear Algebra: Understand vectors, matrices, and operations like matrix multiplication, which are critical for neural networks and data transformations.
Calculus: Master derivatives and integrals, particularly for optimization techniques like gradient descent, used in training models.
Probability and Statistics: Learn probability distributions, hypothesis testing, and metrics like mean and variance to analyze data and evaluate models.
Programming: While you can get a job and make a huge contribution with just ML modeling skills, your career opportunities will significantly increase if you know how to write software that deploys AI solutions in real-world applications - it is actually a key responsibility for ML engineers and data engineers. So, it is worth mastering the basics of programming, algorithms, and data structures. Start by learning the programming languages commonly used in AI, such as Python. Python is the most popular language for AI, thanks to its rich ecosystem of libraries like TensorFlow, PyTorch, and scikit-learn. Learning R, Java, or C++ can also be useful depending on the domain. Visualization libraries are helpful too, since you’ll need to explain results clearly.
Step 3. Get Formal Education and Certifications
There are plenty of free resources online to help you get started. While many AI professionals are self-taught, if your goal is to gain a deep understanding of a topic, reading scattered web pages will not be effective. Though not an absolute requirement, a logically structured course or even pursuing a degree is often a more effective method of learning, and it can help accelerate your career. In one of our previous blog posts we have already explored some of the Best AI Courses and Certifications. Once you have mastered the knowledge offered by the courses, you can move on to reading scholarly articles and other resources.
So, the options you have include:
University Degrees: Degrees in computer science, data science, or AI-focused programs offer all-encompassing training, particularly for research roles like AI research scientist. These are ideal if you aim for deep theoretical expertise but require significant time and investment.
Online Courses: Platforms like Coursera, edX, Udemy, and fast.ai offer beginner-to-advanced AI specializations, ideal for exploring machine learning, NLP, or computer vision. Start with AI for Everyone by Andrew Ng (Coursera) or Google AI for Anyone (edX) to explore core concepts, then deepen expertise with topics like Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera). Many courses can be audited for free or accessed with financial aid.
Certifications: These are industry-recognized credentials that validate your skills for roles like AI engineer or NLP engineer. Notable options include:
IBM Applied AI Professional Certificate: Teaches AI applications like chatbots, aligning with NLP engineer roles.
USAII Certified Artificial Intelligence Engineer/Consultant/Scientist: Technical credentials for building and deploying AI models, suited for AI engineers or researchers.
DeepLearning.AI’s Deep Learning Specialization: Covers neural networks and computer vision, ideal for computer vision engineers.
ARTIBA Artificial Intelligence Engineer: Emphasizes practical AI implementation, relevant for ML engineers.
MIT Artificial Intelligence: Focuses on AI’s business applications, ideal for AI product managers.
Step 4. Stay Updated With Industry Trends
No one can learn everything they need to know in a weekend or even a month. Besides, given how quickly AI evolves, to succeed in this filed, you need to become a lifelong learner. If you can get into the habit of learning a little bit each week, you can make significant progress with seemingly less effort.
So stay current, you need to regularly:
Follow AI researchers and engineers on platforms like LinkedIn, Twitter (X), or Medium.
Read AI-focused publications and blogs.
Take online courses and webinars on the latest AI technologies, experiment with new tools and frameworks as they are released.
Join AI communities, attend meetups, conferences, and workshops.
Step 5. Build Hands-On Experience Through Internships or Personal Projects
Applying theoretical knowledge to real-world problems is a key step in learning AI. One of the most effective ways to gain experience is through Internships. An AI internship is a temporary position often available at tech companies, startups, or research labs, where you gain hands-on experience and receive firsthand exposure to the field. Search for opportunities on LinkedIn, Indeed, or university career centers.
If you can’t get a formal internship, don’t worry. You can gain experience on your own. Here are some ways:
Personal Projects: Build a portfolio on GitHub with small ML or AI projects. For instance, build a chatbot, create a simple recommendation system, or train an image classifier. Recruiters often check this first. Your projects don’t have to be huge. Your main goal is to show your skills, coding ability, and independence.
Hackathons: Join AI or general coding hackathons, time-bound, intensive events that are held in-person or virtually, to apply machine learning, NLP, or computer vision techniques. Some hackathons provide a specific problem or dataset for all participants, others set a broad theme, allowing participants to define their project. Check Devpost, Hackerearth, Lablab.ai, or local developer groups for events. Hackathons are a great way to learn, collaborate, and showcase your skills.
Coding Challenges: Take part in fully online coding competitions on platforms like Kaggle, DrivenData, or CodinGame. These challenges involve solving problems, like predicting outcomes from data, to help you learn and impress employers. Start with beginner-friendly Kaggle competitions, such as the Titanic dataset where you predict Titanic survival. Explore datasets from the UCI Machine Learning Repository (e.g., Iris for clustering) to build familiarity with diverse problems.
Step 6. Build a Portfolio and Network
To stand out in the AI job market:
Create a portfolio – showcase your projects on GitHub or build a personal website.
Write case studies or blogs about your projects to demonstrate your problem-solving approach.
Network with professionals – attend AI meetups, webinars, and conferences like NeurIPS or CVPR.
Join AI communities on Discord, Slack, or Reddit for support and opportunities.
Step 7. Apply for Entry-Level Roles
Start applying once you feel ready. Enhance your resume to highlight your AI skills, projects, and experience. Prepare for technical interviews by practicing problem-solving, programming, and gaining a deep understanding of AI concepts. Post your resume and start looking for entry-level or junior positions that are a good fit. If you are having difficulty finding a job, you can try freelance projects while building your network. Apply for many positions that interest you, even if you don’t meet every single requirement. And even if the role isn’t purely AI-focused, experience in data analysis, software development, or analytics can help you move toward AI opportunities.
Final Thoughts
AI is already part of our world, and knowing how it works and how to implement AI solutions opens the door to some of the most exciting and in-demand careers today. Build strong foundations, gain hands-on experience, work on projects to apply your knowledge, and don’t forget to showcase your experience. While starting a career in this field is challenging, the opportunities are immense. Start small, keep learning, network, and most importantly, don’t get discouraged by setbacks. With persistence and passion, you’ll find your way into the exciting world of AI.
FAQ
Do I need a degree to start a career in AI?
Not necessarily. Many AI professionals come from self-study and online courses. A degree can help, but hands-on skills and projects often matter more.
What’s the difference between an AI course and an AI certification?
Courses focus on learning and building knowledge, while certifications provide official recognition of your skills, often through exams or graded projects. The latter are valued by employers.
How do I choose the right AI specialization?
Focus on your interests. If you enjoy language-related tasks, pursue NLP. If visuals interest you, focus on computer vision. Try small projects, such as a Kaggle competition to test your preference before specializing.
Can I get a job in AI without strong math or programming skills?
It is possible. While math and programming are important, many beginner-friendly tools and frameworks allow you to start experimenting without deep technical skills. However, to open the doors to advanced roles like ML engineer or AI research scientist, you need a solid grasp of math and programming basics.
Which programming languages should I learn first?
Python is the most common language for AI. It is highly popular for its simplicity and strong AI libraries.
Which skills should I learn first in AI?
Start with Python, mathematics (linear algebra, probability, statistics), and machine learning basics before moving on to advanced AI topics.
Do all AI courses include certifications?
No. Some courses only provide training, while others (like Coursera, edX, and IBM) offer official certificates upon completion.
What’s the best way to gain practical AI experience?
Try creating personal projects on GitHub or join Kaggle competitions. If possible, pursue internships through platforms like LinkedIn or university career centers. Hackathons on Devpost also offer collaborative learning opportunities.
