Introduction: Artificial intelligence is now among the most in-demand skills in the IT job market. Despite the general economic slowdown, as many as 88% of companies plan to continue hiring IT specialists, and AI-related competencies are at the top of the demand list. Globally, the demand for AI experts is even greater – experienced machine learning (ML) engineers can command hundreds of thousands of dollars per year in salaries. A career in AI is therefore a future-proof choice. This guide explains what to study, which skills to develop, where to learn, and how to gain experience to become an AI specialist. We also clarify what prompt engineering really is and highlight a paradox – this new niche is partly being automated by AI systems themselves.
What to Study to Become an AI Specialist?
The first step toward a career in AI is the right education. Computer science studies (or related fields such as mathematics, automation, or physics) provide a solid theoretical and practical foundation. In Poland, artificial intelligence can be studied at many renowned universities – for example, the Warsaw University of Technology offers bachelor’s and master’s programs in computer science with an AI specialization (in Polish and English), the University of Warsaw offers a master’s in Machine Learning, and Kozminski University runs the “Bachelor in Management and AI” as well as the “AI in Business” master’s program. Similar programs are also available at institutions such as the Poznań University of Technology and Wrocław University of Technology.
Is a master’s degree worth it? It depends on your goals. A master’s diploma (especially in AI/ML) may help if you’re considering research or specialized R&D positions. However, in the dynamic IT industry, employers increasingly value practical skills and projects over degrees. It is possible to become an AI engineer without a formal diploma, as long as you acquire the right knowledge and showcase a project portfolio. Many people with a bachelor’s degree move directly into the job market or choose postgraduate programs/bootcamps instead of a master’s.
Bootcamps are intensive practical courses – for example, in Poland there are Machine Learning Engineer bootcamps (e.g., at Software Development Academy) or practitioner-led workshops such as DataWorkshop. They provide quick entry into specific skills, though they are fee-based. Projects and internships: Regardless of formal education, hands-on projects and experience are key (more on that below). Often, real-world projects are more valuable than additional diplomas – a practical portfolio is often more important to recruiters than a stack of certificates. That’s why a mixed path makes sense: solid theoretical foundations from studies + practical skills from courses, projects, and internships.
What Skills Are Needed in AI?
An AI specialist combines computer science knowledge with mathematics and data analysis skills. Here are the key competencies you should develop:
Strong foundations in mathematics and statistics: Linear algebra, calculus, and statistics are essential for understanding machine learning algorithms. Topics like vectors, matrices, derivatives, and probability distributions from the early years of study are directly applicable. Without them, it’s difficult to grasp neural networks or model optimization.
Programming (mainly Python): Python is the number one language in AI. You must know its syntax well, understand object-oriented programming, work with virtual environments, and write unit tests. Also important: Python data libraries such as NumPy, Pandas (data manipulation), and Matplotlib/Seaborn (visualization). SQL basics are useful (databases), and R can help if you’re heading toward data science.
Machine Learning (ML): Understanding classic ML algorithms (regression, classification, clustering), model validation methods (e.g., cross-validation), and evaluation metrics is essential. ML theory allows you to build predictive models and solve business problems on tabular data. A great starting point is the legendary Andrew Ng Machine Learning course on Coursera.
Deep Learning (DL): A subfield of ML focused on neural networks. Learn about CNNs (images), RNNs/LSTMs (sequences), and Transformers (language and beyond). Practical mastery of one major DL framework (TensorFlow/Keras or PyTorch) is a must. A great, hands-on introduction is the free fast.ai – Practical Deep Learning course.
MLOps and cloud basics: Deploying models in production requires MLOps skills. Learn tools such as Docker (model containerization), CI/CD automation (e.g., GitHub Actions), and experiment monitoring systems (MLflow, Kubeflow). Familiarity with at least one public cloud (AWS, GCP, or Azure) is increasingly expected – e.g., running models in the cloud or using services like AWS SageMaker. Basic Linux admin and scripting skills are also valuable.
Domain specialization: Once you master the basics, dive deeper into a specialization such as NLP (natural language processing), Computer Vision, RL (reinforcement learning), or the hot area of Large Language Models (LLMs) and prompt engineering. Specialization helps you stand out and work on the tasks that interest you most.
Other useful skills: Analytical thinking and problem-solving are crucial – AI often requires experimentation and iterative approaches. English proficiency is a must (most AI documentation and research is in English). Knowledge of AI ethics and current regulations (e.g., the EU’s AI Act coming into force in 2025) is also becoming important.
Where to Learn? Courses, Platforms, and Certificates
The AI field evolves so rapidly that learning often happens independently through online courses and open-source materials. Here are some proven sources:
Free online courses: The mentioned Andrew Ng Machine Learning course (Coursera) is a classic for beginners. For more advanced learners, the Deep Learning Specialization (Coursera) or Fast.ai (free practical DL course) are excellent. Also check platforms like edX, Udacity (e.g., AI Nanodegree), or Kaggle Learn – many offer free access. A great Polish initiative for beginners is Warsaw.ai Academy – “AI in 8 weeks”, combining meetups and online resources.
Postgraduate and academic programs: If you prefer formal paths, consider a master’s in AI/Data Science. In Poland, you’ll find programs such as Artificial Intelligence (Warsaw University of Technology) or Data Science (Wrocław University of Technology), often in English and research-oriented. Universities increasingly offer postgraduate AI/ML studies, lasting 1–2 years and focused on practice.
Bootcamps: As noted, bootcamps are intensive courses lasting a few months, taught by practitioners. In Poland, examples include the Software Development Academy (SDA) course for Machine Learning Engineer or DataWorkshop run by industry experts. Bootcamps are paid but provide structured learning, mentor support, and often job interview prep.
MOOC platforms and open-source resources: Beyond the courses above, follow platforms like GitHub for open resources – e.g., awesome lists curated by the community (Awesome Machine Learning), Jupyter notebooks with model examples, or free e-books. Hugging Face also offers plenty of tutorials and courses, especially around language models. Even official library docs (TensorFlow, PyTorch, scikit-learn) often include tutorials.
Certificates: Certifications can strengthen your credibility, though they don’t replace a portfolio. Consider cloud-related AI certs such as Google Professional ML Engineer, AWS Certified Machine Learning – Specialty, or Azure AI Engineer Associate. There are also specialized ones like the TensorFlow Developer Certificate. Remember: certificates are supplements – your actual skills matter most.
Communities and conferences: Learn beyond formal courses. Join communities like ML in PL, or take part in AI hackathons. Conferences (e.g., Data Science Summit, ML in PL Conference) are great opportunities to hear from experts, track trends, and network (often with discounted or free student access). Networking can later lead to internships or job offers.
How to Start Gaining Experience?
Practical experience is invaluable for breaking into the AI job market. Here are some strategies for building it already during your studies:
Work on your own projects: Nothing teaches you better than creating your own models from scratch. Choose a problem that interests you – e.g., image recognition, sentiment analysis in texts, or a recommendation system – and try to solve it with an ML model. Collect data (public datasets are available, e.g., on Kaggle), train models, compare results. Publish these projects on GitHub. Three solid repositories (e.g., image classification, text analysis, and an MLOps pipeline) in your portfolio impress recruiters. Make sure the code is clean, and in the README describe your approach and conclusions.
Competitions and tasks on Kaggle: Kaggle is a platform with data science/ML competitions where you can work on real-world data and compete with others. Even if you don’t make it to the leaderboard, participation will teach you a lot – from data preprocessing and feature engineering to model tuning. Start with beginner-friendly competitions (Titanic, House Prices), which come with tutorials. Achieving even a bronze medal or >1000 Kaggle points shows you can apply your knowledge in practice. Alternatively, platforms like DrivenData or AIcrowd also provide valuable experience.
Contribute to open source: Many AI tools are open-source – you can try contributing. Fixing a small bug in TensorFlow/PyTorch libraries or adding functionality to a GitHub project will teach you best practices and collaboration with other developers. It also signals to recruiters that you’re engaged in the community.
Internships and placements: Look out for internships in Data Science/AI departments. Tech companies, banks, and even AI startups often run summer programs for students. Internships let you tackle real business problems under the guidance of mentors – an invaluable experience. During your placement, aim to actually build a model or analyze data, so you can later talk about it in your CV and interviews.
Build a portfolio and share your knowledge: Present your projects to the world. Organize your portfolio – GitHub with code, optionally a personal website with project descriptions. Consider starting a technical blog or posting on LinkedIn – explain in plain language what you’re working on, what you’ve learned, and what results you achieved. Writing or recording about AI helps you practice clear communication of complex topics (a valuable skill itself). This activity builds your personal brand and shows passion. You could also propose a talk at a meetup/conference for beginners – stepping up and presenting your project gives you exposure and new contacts.
Remember, AI recruitment is all about what you can build. That’s why, during your learning journey, focus on creating practical solutions using real-world data, because such experience cannot be replaced by theory from books. It’s also good to show awareness of context in your portfolio – e.g., if you’re building a model, did you account for ethics and regulatory compliance (a new requirement entering industry standards)? Such details make you stand out as a mature candidate.
Practical projects (from data analysis to model deployment) are the best training ground for a future AI specialist.
What Is Prompt Engineering in the AI World?
In recent years, a new term has emerged – prompt engineering. What does a prompt engineer actually do? In short, it is the art of designing and optimizing queries for large language models (LLMs), such as GPT-4 or PaLM, to get the desired responses. It’s more than just writing a random command – it requires consciously crafting prompts, testing variations, and refining them to make the most of the model’s capabilities.
A classic prompt is simply a text instruction (optionally with context or examples) given to the model. Prompt engineering means phrasing the instruction so that the model understands your intent as precisely as possible and generates an appropriate response. This often involves providing the right context, breaking down a complex task into steps, or giving hints about the response format. Example: instead of writing “Summarize this”, an engineered prompt would be: “List the key takeaways from the following text in bullet points, using a formal style.” Notice how it specifies both content and form clearly.
A good prompt engineer combines technical knowledge with a sense of language. They blend logic, programming, and even creative writing. They must understand how modern language models work, their limitations, and the use of modifiers (e.g., special tokens or phrases that influence the tone and style of responses). Prompt engineering also involves experimentation – the same model can react differently to seemingly similar prompts, so specialists test many variants to find the optimal one. Often, entire interaction strategies are created – for example, first instructing the model to assume a certain role (“You are an experienced tax advisor...”), then providing input data, then asking for step-by-step analysis. All of these are elements of prompt engineering.
Is prompt engineer a separate profession? Currently, some companies do indeed hire such specialists full-time, and the media have hailed prompt engineering as the “job of the future”. In the U.S., there were even offers exceeding $300,000 annually for prompt engineers at AI product companies. But opinions about the long-term future of this role are divided. Increasingly, it’s seen as an important skill, but not necessarily a permanent, standalone profession. In practice, effective prompt writing is useful for many AI roles – from LLM engineers to data scientists to developers building generative AI apps. It’s similar to Excel – almost everyone in an office uses it, but very few call themselves “Excel engineers.”
The Paradox: AI Automates Prompt Engineering
It’s worth noting a paradox: the niche of prompt engineering is beginning to be automated by AI itself. On the one hand, people say prompt engineers are the jobs of the future; on the other, tools and techniques are already emerging that can improve or even partially replace manual prompt creation.
Sam Altman (CEO of OpenAI) even said, “in 5 years we won’t be doing prompt engineering”, as AI interfaces will become more user-friendly – we’ll simply tell the model what we want, without complicated phrasing. Models are getting better at understanding natural instructions, so the need for elaborate prompts may decrease. Moreover, research has shown that AI can generate more effective prompts than humans – the creators of the DSPy library (for prompt optimization) even declared that “prompt engineering is dead”, showing examples where a model automatically crafted a better prompt than experts. In other words, AI can help write prompts for AI!
Tools are emerging to support this process, such as GPT-Engineer for automatically generating prompts for coding tasks or PromptPerfect for optimizing prompts. Frameworks like DSPy let AI engineers work at a higher level of abstraction – they define only what they want to achieve (inputs/outputs and success criteria), and the algorithm itself generates and tests different prompts to find the best one. Studies suggest that hybrid approaches (AI generates initial prompts, humans fine-tune them) yield the best results.
What does this mean for someone planning a career in AI? Above all, that you need to stay up to date with trends. Prompt engineering as a standalone role may fade, but the ability to communicate effectively with models will remain valuable. Generative AI will become more widespread, and those who can use it effectively (whether by writing good prompts or by using automation tools) will be in demand. Paradoxically, the more AI simplifies prompt creation, the higher the bar is raised – AI professionals must keep learning and adapting new methods of working with models. That’s why you should treat prompt engineering as one skill in your toolbox, not your only differentiator.
Conclusion
A career in AI is demanding but also extremely exciting. Let’s recap the key advice:
Education: Build a strong foundation – technical majors (computer science, math, etc.) provide the base. A master’s in AI/ML can help but isn’t required – skills matter more. Complement your studies with online courses or a bootcamp for hands-on practice.
Skills: Master math, Python programming, and ML fundamentals. Then go deeper into deep learning, MLOps, and cloud. Specialize in a niche (e.g., NLP, CV). Keep improving – in AI, learning never ends, as new techniques appear every few months.
Practical learning: Use online courses (Coursera, fast.ai, etc.). Read blogs and open-source docs. If possible, study at institutions offering AI programs. Earn certificates as extras, but focus on applying your knowledge.
Experience: Build a portfolio of projects – academic, personal, and collaborative. Join Kaggle competitions or open-source projects. Try to get an internship to see AI in a business context. Your projects, code, and solved problems are your best resume.
Prompt engineering: Understand how generative models work and practice crafting prompts for them. Know it’s not magic, but an iterative optimization process. As AI evolves, prompt engineering will simply become another valuable skill – important, but supported by tools.
Stay flexible and follow trends: AI evolves rapidly. What’s hot today (like prompt engineering) may be automated tomorrow. Keep learning passionately, stay open to change, and broaden your horizons. Combining AI skills with domain knowledge (e.g., healthcare, finance) will further increase your value.