How to Become and AI Engineer

Nedko Krastev

Udemy Instructor Partner

Graphic displaying a man working at his computer on a technical project

Artificial intelligence (AI) is no longer a futuristic concept. It’s reshaping almost every industry and rapidly becoming embedded in our daily lives.

According to the World Economic Forum, jobs requiring AI or machine learning skills are expected to grow by 71% between 2021 and 2025 ^[1]. Meanwhile, the salary range for AI engineers in 2025 is around $108,000 – $174,000 ^[2], a clear sign of surging demand and limited supply of qualified professionals.

With AI rapidly evolving, there simply aren’t enough trained AI engineers to fill the growing number of roles. If you’re curious about AI and eager to help shape the future of technology, there’s never been a better time to explore this career path.

The good news? You don’t need a degree from an elite university to get started. Through beginner-friendly online courses, you can go at your own pace, build a strong portfolio, and begin a nontraditional but highly effective path into AI engineering without the burden of formal, expensive education.

What Does an AI Engineer Do?

AI engineers are responsible for developing, deploying, and maintaining AI-powered applications—not necessarily building foundational AI models from scratch, but integrating them into real-world products.

Their primary focus is to bridge the gap between advanced AI models (like those developed by OpenAI or Google) and practical applications such as websites, mobile apps, IoT devices, or smart home systems. In doing so, AI engineers:

Integrate foundation models (such as large language models or vision models) into software products and services.
Enhance performance through techniques like prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
Ensure model safety, scalability, and user experience, often working closely with product managers, data engineers, and DevOps teams.
Utilize data pipeline to allow for ongoing learning, personalization, and adaptability in deployed systems.

It’s important to note that AI engineers are not the same as LLM developers or data scientists. LLM developers typically work at major AI research labs and are responsible for designing algorithms and training large-scale models from scratch. Data scientists focus more on statistical analysis, experimentation, and custom machine learning models based on structured data.

AI engineers spend most of their time researching methods for integrating AI into a company’s existing and new products, coding and experimenting with various foundation models (LLMs), and fine-tuning these models to enhance performance. They must be well-versed in problem-solving and troubleshooting to identify and resolve issues.

They also collaborate with other departments, including product managers, software engineers and business executives, to ensure their AI implementation fits company goals. This usually requires AI engineers to collaborate closely with their colleagues on the product team.

Today, it is increasingly rare for people to design AI from the ground up. Instead they tend to build on top of existing foundation models. This is precisely the AI engineer role:

Data accuracy. AI training utilizes large datasets. If the data has any issues or inaccuracies, the AI learns the wrong information. All data must be of the highest quality for success, which means it needs to be reviewed.
Integrating with legacy systems. Many businesses still utilize legacy systems, which may not have the capacity to integrate with those developed by AI engineers. These engineers must either develop and utilize middleware to support AI usage with existing software, or perform a total system overhaul to modern equipment.
Privacy and ethical considerations. AI poses ethical concerns. If the system receives biased training data, it’s likely to make decisions based on those biases. Because AI is still a relatively new field, concerns about privacy, discrimination, and other ethical issues remain largely unregulated.
Constant changes. Since AI is still an emerging field, it’s undergoing constant changes. AI engineers must keep up-to-date with new regulations, standards, and advancements as they emerge. This requires an agile process that allows for continuous learning, research, and collaboration.

Although it requires extensive effort, AI engineering is a rewarding career. AI engineers contribute to:

Enabling hyper-personalization that scales with businesses by designing models capable of processing a user’s data and applying it immediately to make personalized recommendations
Embedding intelligence into core products to improve usability, accessibility, and effectiveness
Supporting environmental, social, and governance (ESG) growth, helping businesses meet sustainability goals
Enhancing customer support beyond AI chatbots
Help businesses integrate state-of-the-art AI technology into existing and new products.
Enable hyper-personalization of a company’s product offerings.
Experiment with, and fine-tune, foundation models (LLMs) for company-specific use cases. For instance, we’ve collaborated with a diet company to implement state-of-the-art AI that responds to client chat messages in real time.

When 78% of organizations report using AI in some capacity in 2024, compared to 55% in 2023, it’s clear that AI is here to stay^[3]. Top industries hiring AI engineers include:

Technology
Finance
Health care
Education
Automotive
Transportation
Logistics

Skills Required to Become an AI Engineer

AI engineering is a highly technical role that requires strong skills in math, coding, and computer science. To succeed in this field, you’ll need to develop expertise across several key domains:

Programming Languages
AI engineers require strong coding skills, especially in Python, which powers the majority of AI and data science libraries. Python allows you to script data analysis, explore datasets, and prototype models efficiently. Libraries like pandas and NumPy are essential for data preparation and manipulation.
Math and Statistics Fundamentals
A solid foundation in linear algebra, calculus, probability, statistics, and graph theory is crucial for understanding how AI models process and learn from data.
Machine Learning, Deep Learning, and NLP
These are the building blocks of AI systems. Machine learning enables AI to recognize patterns and make predictions, deep learning powers more complex tasks like image and speech recognition, and natural language processing (NLP) allows AI to interact with human language.
AI Frameworks and Tools
Engineers use powerful tools like TensorFlow and PyTorch to build, train, and deploy machine learning models. Familiarity with these frameworks speeds up development by leveraging pre-built components and architectures.
Foundational Model Integration
AI engineers often work with foundation models rather than building models from scratch. Platforms like Hugging Face offer libraries such as Transformers, which provide access to state-of-the-art LLMs that can be fine-tuned for specific business tasks.
LLM Orchestration and Tooling
Tools like LangChain are indispensable for integrating LLMs into real-world products. LangChain allows engineers to chain together components, switch between different LLMs, and create dynamic AI applications with ease.
Vector Databases
Technologies like Pinecone support the storage and retrieval of embeddings—high-dimensional representations of data used in modern AI systems. These databases are optimized for speed and scalability, making them essential for building responsive and intelligent applications.
Cloud Platforms and DevOps Tools
Experience with cloud providers like AWS, Azure, or Google Cloud enables AI engineers to deploy models at scale. Skills in containerization tools like Docker are also valuable for creating portable, scalable AI environments.
Soft Skills and Ethical Awareness
Effective communication, problem-solving, and collaboration are key for working on cross-functional teams. AI engineers must also be attuned to data ethics and bias, especially when deploying models that affect real users.

Showcasing Your AI Skills to Land a Job

Although AI engineers often possess a master’s degree or PhD in related fields, such as computer science, a degree isn’t always necessary. Professional certificates and self-study can be enough to get your first role. However, this nontraditional path requires you to have the right collection of skills with a portfolio and resume to back it up.

Build a Standout AI Portfolio

With such rapid growth and earning potential in the industry, AI engineers face steep competition for positions. Creating a portfolio that stands out is one of the most effective ways to set yourself apart.

As you build your portfolio, include end-to-end projects that demonstrate your skills. They should emphasize an understanding of real-world problems and how you applied AI solutions to solve them. Create a portfolio that highlights a range of techniques relevant to the positions you seek, using tools like LangChain, VectorDB, Pinecone, FastAPI, OpenAI API, and Hugging Face.

Don’t stop at the end product. Showcase the inner workings of your project by hosting your demo project on Streamlit or personal websites. This displays that you have the technical skills necessary for success in the industry.

What to Highlight on Your Resume

An AI engineer’s resume should be industry-specific and metrics-driven. While listing past projects is a start, it’s essential to quantify the results of your efforts. For example, instead of writing that you worked on a project, say something like, “improved model accuracy by 12% using hyperparameter tuning.” This clearly states your contribution in a quantifiable context.

Include your proficiency in core skills and tools, such as Python and LangChain for coding and development, Hugging Face and OpenAI API for foundation model integration and fine-tuning, AWS SageMaker and Google Cloud AI Platform for cloud hosting and deployment, and Docker and Kubernetes for containerization and orchestration. Some hiring managers may prioritize prospective hires with experience using tools relevant to their upcoming projects. Detail any relevant coursework, certifications, and competitions you’ve participated in.

Tips to Stand Out in the Job Search

It’s not always enough to have the hard skills necessary for success. Hiring managers in AI-driven industries seek practical thinkers with a passion for continuous learning, which can be difficult to showcase through a job application. To stand out, try:

Posting on LinkedIn or Medium, sharing a project breakdown that demonstrates thought leadership.
Tailoring your portfolio and resume to the role you seek. Present projects, skills, and education that match the job description.
Continuously seek learning opportunities through courses and certifications, and update your resume accordingly.

Educational Pathways and AI Engineer Certifications

Many AI engineers have a formal education in computer science, data science, and similar fields. While a bachelor’s degree often serves as a point of entry, many roles require more advanced credentials. However, online learning through courses such as our Intro to ChatGPT and Generative AI course can also help you break into the field.

Online, skills-based courses and certificates provide a distinct advantage over a traditional degree — you can take them at your own pace. Active participation and learning show that you have the drive for continuous education necessary for success in this rapidly evolving field.

Gaining Hands-On Experience in AI

Hands-on AI experience helps you gain real-world skills while building a portfolio and enhancing your resume. As an aspiring AI engineer, you can gain this experience by:

Participating in AI competitions and challenges. Kaggle competitions challenge AI engineers to solve specific issues. All participants receive a specific dataset and problem, and they must identify a solution using data and machine learning techniques. This helps beginners and experts hone their skills. It also allows you to create more projects to display in your portfolio and provides a valuable networking opportunity.
Building portfolio projects with real-world use cases. If you have a specific project in mind, consider working on it during your spare time. Even if you won’t be paid for the work, you’re compensated with experience and another successful project to showcase in your portfolio.
Applying knowledge through capstone projects. Many online courses guide you through capstone projects to ensure you have a strong understanding of foundational topics.
Freelancing. Working as a freelancer opens the door for you to consult and collaborate on real projects. You can network while gaining proven work experience for your resume.

Exploring Career Paths in AI Engineering

AI engineers carry a range of job titles depending on their specialty or field. Common career paths include:

AI engineer: AI engineers build and deploy AI-powered applications by integrating pre-trained models into real-world products like apps, websites, and smart devices. They optimize model performance through techniques like prompt engineering, fine-tuning, and data pipeline development to ensure intelligent, scalable systems.
Machine Learning engineer: ML engineers specialize in developing and deploying machine learning models that learn from data and make predictions or decisions based on it.
Natural Language Processing engineer: NLP engineers ensure computers and AI programs can understand and utilize human language. They play a key role in developing chatbots, translators, and digital assistants.
Data scientist: Data scientists support AI engineers by analyzing and interpreting large datasets. AI engineers rely on accurate data to train their models, and data scientists play a crucial role in providing it.

As AI becomes increasingly capable of analyzing and interpreting data almost instantaneously, many fields utilize it to retain a competitive edge. Many fulfilling AI engineering career paths await in the following industries:

Health care: The health care sector can utilize AI for diagnostics, treatment planning, and patient care. Because an AI model can analyze more data at once than a person, it can provide tailored recommendations based on a patient’s history, test results, and predicted outcomes.
Finance: AI can assist with predictive algorithmic trading and fraud detection. It also supports quicker, 24/7 customer service with AI-powered chatbots and virtual assistants that provide custom, data-driven recommendations.
Logistics and manufacturing: AI advancements automate tedious tasks in logistics and manufacturing. AI engineers and their projects improve demand forecasting, route optimization, inventory management, quality control, and overall supply chain optimization.
Retail and e-commerce: AI engineers can design systems that provide tailored recommendations based on browsing behavior and purchase history. This puts relevant products in front of customers more likely to purchase, boosting sales.

How Udemy Can Help You Become an AI Engineer

On Udemy, you can enroll in courses designed to kickstart your AI engineering career. Our courses are led by industry experts and designed with flexibility in mind. Whether you’re a beginner starting from scratch or an expert looking to brush up on new skills, you can find certification options relevant to your experience and needs. Check out our Artificial Intelligence courses today and join the millions of learners who’ve already taken their first step into this exciting career path.