How to Get Into AI: Jobs, Career Paths, and How to Get Started (2025)
Learn how to get into AI with a clear, expert-backed roadmap—career paths, must-have skills, real-world tips, and projects to land your first AI job.
Posted November 11, 2025
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Artificial Intelligence (AI) is no longer a niche; it's the engine powering nearly every major industry, from healthcare and finance to entertainment and robotics. But if you’re wondering how to get into AI in 2025, you're not alone. Whether you’re switching careers, starting from scratch, or coming from a non-technical background, this guide will break down real steps to launch a successful AI career.
We’ll cover key roles like AI engineer, machine learning engineer, and data scientist, explore essential AI skills, and share insider tips pulled from top forums and real-world practitioners. Let's dive in.
Why AI? Why Now?
In the last decade, AI technologies have evolved from basic automation tools to advanced AI systems capable of natural language processing, computer vision, and even generative AI. The rise of AI models like ChatGPT and DALL·E has made it clear: the future belongs to those who understand how to build, manage, and improve AI and machine learning systems.
Companies across every sector are actively hiring AI professionals to develop smarter products, personalize user experiences, and automate complex workflows. According to recent industry reports, demand for roles like machine learning engineers, AI engineers, and data scientists will continue to outpace supply well into the next decade.
Read: How to Become an AI Specialist
AI Career Paths: Roles, Responsibilities, and Skills
| Role | Key Responsibilities | Core Skills | Ideal Background | Best For |
|---|---|---|---|---|
| AI Engineer | AI engineers build and deploy AI models and AI systems that power intelligent applications. They apply deep learning, generative AI, and reinforcement learning techniques, and often work closely with software and data teams to bring models into production. | Proficiency in Python and cloud platforms such as AWS or GCP is essential. A solid understanding of neural networks, AI algorithms, and model deployment is also required. | A degree in computer science or software engineering is common, though many enter through machine learning bootcamps or hands-on experience. | This role is ideal for technically inclined professionals who want to build real-world AI solutions that ship and scale. |
| Machine Learning Engineer | Machine learning engineers design, train, and optimize machine learning models. They focus on developing algorithms, working with large datasets, and fine-tuning model performance in production environments. | Key skills include Python, TensorFlow or PyTorch, strong coding foundations, knowledge of unsupervised learning techniques, and a deep understanding of machine learning methods. | Candidates typically come from computer science, data science, or applied machine learning backgrounds with hands-on project experience. | Best for those who enjoy experimentation, iteration, and solving complex technical challenges using data. |
| Data Scientist | Data scientists use data analysis, statistical methods, and machine learning to uncover insights and guide decision-making. They often build simple predictive models and support AI system development through data visualization and analysis. | They must be fluent in tools like Python, SQL, and Tableau, with strong capabilities in data manipulation, data mining, data literacy, and statistical analysis. | A background in mathematics, statistics, economics, or analytics, combined with technical skills, is common. | This role suits curious, analytical thinkers who enjoy uncovering patterns in data and translating them into business impact. |
| Research Scientist (AI/ML) | Research scientists work on the frontier of AI, developing new algorithms and advancing the field through original research. They often publish in academic journals and prototype novel machine learning systems. | This role demands deep theoretical knowledge of machine learning, reinforcement learning, and deep learning frameworks, along with strong programming and math skills. | A PhD or master’s degree in computer science, artificial intelligence, or electrical engineering is usually required. | Best for academically driven individuals who want to contribute to the long-term evolution of AI technologies. |
| AI Product Manager / AI Professional | AI product managers guide the development of AI products by defining requirements, working cross-functionally with engineers and scientists, and ensuring the solution addresses real user problems. They focus on strategy, ethics, and delivering value through AI. | Core strengths include communication, project management, a basic understanding of machine learning principles, and the ability to translate between technical and non-technical teams. | Professionals with hybrid experience in product management, analytics, or business strategy often thrive in this role. | Ideal for those who enjoy connecting dots across disciplines and ensuring AI technology delivers real-world impact. |
What Essential Skills Do You Need to Get Into AI?
You don’t need a PhD, a Silicon Valley background, or 10 years of coding experience to break into AI. But you do need a strong foundation across several technical and domain-specific areas, plus the ability to apply them to real-world problems.
Technical Foundations
These are the must-have skills that power every AI career, whether you're pursuing a role in machine learning engineering, data science, or AI engineering.
- Programming languages - Python is the industry standard for AI and machine learning, thanks to its simplicity and rich libraries (like NumPy, Pandas, and TensorFlow). R, Java, and C++ are also useful depending on your focus area.
- Mathematics - You need a working knowledge of linear algebra, calculus, probability, and statistical analysis, especially if you're aiming for research or model optimization roles.
- Data structures and algorithms - A solid grasp of computational thinking is essential for writing efficient code and building scalable AI systems.
- Machine learning fundamentals - Learn supervised, unsupervised, and reinforcement learning techniques, including how to choose and evaluate the right machine learning algorithms.
- Deep learning and neural networks - Understand how deep learning frameworks like PyTorch or TensorFlow are used to build powerful AI models.
- Cloud computing and deployment - Most AI solutions run on platforms like AWS, GCP, or Azure. Knowing how to deploy AI models in production is a high-value skill.
- Data analysis and visualization - Strong skills in data manipulation, data visualization tools, and database management systems will help you explore, clean, and communicate your findings effectively.
AI-Specific Skills
These domain-specific skills will differentiate you as a true AI professional and make you competitive for roles across industries.
- Natural language processing (NLP) - Used in applications like chatbots, translation tools, and search engines. Learn how to build and evaluate NLP AI models.
- Computer vision - Powers systems like facial recognition, autonomous vehicles, and medical image diagnostics. Involves AI algorithms for object detection, classification, and segmentation.
- Generative AI and emerging models - Understand how generative AI models (like GPT and diffusion models) are created and applied in the real world.
- End-to-end project execution - Learn how to scope, build, and deploy AI solutions to solve real-world problems, not just toy datasets.
Read: How to Build an AI Agent From Scratch: The Beginner's Guide
Soft Skills and Work Practices
AI may be technical, but human and strategic skills still matter, especially in cross-functional, production-facing roles.
- Problem-solving orientation - Great AI professionals don’t just optimize models, they apply them to real, messy, business-critical problems.
- Cross-functional collaboration - You'll often work with product managers, engineers, designers, and stakeholders who don’t speak “AI.” Clear communication is key.
- Continuous learning mindset - The field evolves fast. Stay current with research, tools, and new AI technologies through papers, online courses, and hands-on projects.
- Ethical awareness - As AI surpasses human intelligence in some domains, ethical considerations around bias, misuse, and transparency are critical, especially in high-stakes systems.
Read: AI Upskilling: Why It's Necessary & How to Get Started
Want to fast-track your learning? Work 1-on-1 with an AI or machine learning coach on Leland who can help you build a custom learning path, choose the right AI tools, and land your first AI job.
Do You Need a Computer Science Degree?
Not necessarily, but it can accelerate your journey. A computer science degree gives you structured training in core areas like data structures, software development, and AI algorithms, which are foundational for many AI and machine learning roles.
That said, a degree is not a requirement for success in this field. Many high-performing AI engineers, data scientists, and machine learning engineers come from alternative paths, including:
- Online courses and certifications (e.g., Coursera, edX, DeepLearning.AI)
- Bootcamps that focus on applied machine learning techniques
- Self-taught learners who built strong portfolios of AI projects that solve real-world problems
The real key is proof of skill. Employers care more about your ability to build and deploy effective AI models than about your diploma. A well-documented GitHub portfolio, Kaggle competition results, or experience contributing to open-source AI systems can carry just as much (or more) weight as a degree.
If you're coming from a non-traditional background, focus on building real-world projects, learning core AI skills, and clearly communicating your impact in applications and interviews.
How to Actually Get Started (Step-by-Step)
Step #1: Choose the AI Career Path That Fits You Best
Before you dive into coding or courses, get clear on where you want to go. AI is a broad field, and the right path depends on your strengths and interests. If you enjoy building systems, consider becoming an AI or machine learning engineer. If you're more analytical and data-driven, data science might be a better fit. If you're excited by innovation and theory, research could be the right path.
Start by reading real job descriptions and understanding the difference between roles. Clarity here will shape every step that follows.
Step #2: Build Core Technical Skills with Intention
To succeed in any AI role, you need to master the foundational tools and concepts. Begin with Python; it’s the industry standard. Then focus on math essentials like linear algebra, probability, and statistics, which are crucial for understanding how models work under the hood. Learn machine learning fundamentals, from supervised and unsupervised learning to reinforcement learning.
Use platforms like Google Colab or Kaggle to start experimenting with small datasets and models as you learn. The goal isn’t perfection but momentum and practice.
Check out this companion guide: AI for Product Managers: How to Build With It
Step #3: Level Up with Targeted Online Courses and Certifications
Once you’ve built your foundation, deepen your expertise through structured learning. Top platforms like Coursera, edX, and fast.ai offer rigorous courses in deep learning, natural language processing, computer vision, and data engineering.
Look for programs that include hands-on labs or final projects you can showcase later. Certifications can help signal your skills to recruiters, but what matters most is your ability to apply what you’ve learned in real-world contexts.
Step #4: Build Real Projects That Solve Real Problems
AI is a show-me field. Employers want to see what you’ve actually built. Aim to complete three to five strong projects that reflect your interests and show your range. These could include a chatbot powered by natural language processing, an image classifier using computer vision, or a predictive model for a real dataset.
The key is to focus on projects that demonstrate your ability to solve meaningful problems using AI systems and machine learning techniques. not just tutorials you copied.
Step #5: Share Your Work and Connect with the AI Community
Learning in isolation will slow you down. Join active communities like r/MachineLearning on Reddit, contribute to open-source projects on GitHub, and participate in Kaggle competitions. Share your projects and insights on LinkedIn to get feedback and attract opportunities.
Connecting with other AI professionals will expose you to new tools, help you stay motivated, and make you visible to hiring managers and collaborators.
See this guide for executives and team leaders: AI for Business Leaders: How to Use It in Your Company
Step #6: Apply for Internships, Entry-Level Roles, and Freelance Gigs
Once you have skills and projects under your belt, start applying for junior roles in AI engineering, machine learning, data science, or software development. Tailor your resume and portfolio to highlight relevant AI skills and project outcomes.
Use keywords like “develop AI algorithms,” “deploy machine learning models,” or “real-world predictive systems” to match job descriptions. Even internships or part-time freelance roles can be powerful stepping stones if they give you production-level experience.
Expert tip: Advance your AI career with expert guidance from a Google alum. This custom coaching package helps you choose the right AI path, sharpen your technical profile, and land high-impact roles step by step.
Real Advice From People Who’ve Been There
Reddit threads reveal some common pain points for AI beginners:
- "I don’t know where to start. Everything feels overwhelming."
- "I’m not from a CS background. Am I too late to get into AI?"
- "I’ve taken a few courses, but I’m still lost when it comes to real AI work."
What worked for others:
- Focusing on one area first (e.g., computer vision or natural language processing)
- Doing small, tangible AI projects with clear goals
- Pairing courses with hands-on practice
- Finding mentors or communities to stay motivated
Final Tips for Breaking Into AI
- Start small, but stay consistent. Progress in AI compounds over time; don’t try to master everything at once.
- Prioritize machine learning systems and AI tools that solve real-world problems. Companies want people who can apply theory to production.
- Focus on AI engineering skills that companies value. This includes model deployment, working with cloud platforms, and writing maintainable code.
- Use your portfolio to showcase data literacy, technical skills, and creativity. Highlight not just what you built, but why it matters and how it works.
- Remember: employers care more about what you can build than your job title. Output speaks louder than resumes.
- Build 3–5 projects that reflect your ideal role. If you want to be a computer vision engineer, your portfolio should prove it with relevant applications.
- Document your work clearly. Write READMEs, explain your thinking, and show your process, not just your results. It makes you stand out to recruiters and collaborators.
- Stay current with AI technologies and trends. Subscribe to newsletters, follow GitHub repos, or track arXiv papers weekly to stay sharp.
- Engage with the AI community. Share your learning journey on LinkedIn, participate in forums, or contribute to open-source; visibility leads to opportunities.
- Be prepared to explain your work technically and strategically. In interviews, showing that you understand both the how and why behind your models is key.
TL;DR
To get into AI in 2025, you need a mix of technical expertise, project experience, and strategic focus. Start by picking your path (AI engineer, data scientist, or machine learning engineer) and then build a strong portfolio of AI projects that show off your ability to solve real-world problems. With the right mix of skills and support, a career in AI is well within reach.
Ready to get serious about AI? Explore 1-on-1 coaching on Leland to build a learning plan, prep for interviews, and break into top AI careers faster. Find your coach here. Also, check out free events and bootcamps for AI productivity and tooling for more strategic insights!
Read next:
- How to Future-Proof Your Career in The Age of AI
- How to Land an Internship at OpenAI (2026)
- Artificial Intelligence Jobs: Salary Overview by Seniority
- Top 20 Careers in AI & Machine Learning (2026)
- Top 10 AI Certification Programs
FAQs
What should I learn first to get into AI?
- Start with Python, linear algebra, and core machine learning algorithms. Then build small projects using real datasets.
Can I get into AI without a degree?
- Yes. Many AI engineers are self-taught through online courses, projects, and bootcamps. A computer science degree can help, but it isn’t required.
Is AI a good career in 2025?
- Absolutely. Demand for AI professionals, especially in AI engineering and data science, is higher than ever and growing.
How long does it take to become an AI engineer?
- With focused learning and projects, you could land a junior AI job in 6–12 months.
What’s the difference between AI and machine learning?
- AI is the broader field; machine learning is a subset focused on training models from data.
