If you’ve been curious how to become an AI Engineer, the first thing to know is the job has evolved rapidly since generative AI went mainstream. A few years ago, AI engineering looked a lot like traditional machine learning — building and training models from scratch. Today, companies are hiring AI Engineers who can integrate powerful pretrained models, deploy LLM‑powered applications, and ship real AI features that solve real business problems. The job description has changed and so have the skills employers expect.
To keep up with this shift, we built a brand‑new AI Engineer career path at Codecademy. Our curriculum team analyzed current job postings and emerging AI‑focused titles to determine exactly what modern AI Engineers need to know in 2026 and shaped the path around those in‑demand skills.
If you’re ready to understand what an AI Engineer does today, what skills you need, and how to become an AI Engineer yourself, read on. We’ll walk you through the role and everything included in the new career path so you can start building toward this fast‑growing career.
What does an AI Engineer do?
If you’ve spent any time trying to decode the modern AI job landscape, you’ve probably seen titles like AI Engineer, LLM Engineer, AI Full-stack Engineer, and AI Applications Developer. These roles can sound mysterious or even interchangeable, but the core responsibilities share a clear through line: AI Engineers focus on building real, usable AI-powered applications by leveraging the explosion of high-quality pre-trained models now available.
Our team of subject-matter experts thinks of an AI Engineer as a software‑oriented developer who uses and integrates modern AI models — like LLMs and agents — into applications. They don’t build models from scratch; instead, they focus on deploying, understanding, and maintaining practical AI systems using contemporary tools.
Heather Hardway, one of the primary Curriculum Developers behind our new AI Engineer career path, likens the role to AIOps. “So, they’re people who know how to use models and deploy them, track them,” she says. “They’re using APIs and OpenAI, but they might not be on the research side where they’re developing models from scratch.”
Unlike Machine Learning Engineers, who traditionally built models from the ground up, AI Engineers respond to how the field has changed since transformer architectures and open-source models surged into mainstream development. Instead of reinventing the wheel, they select the right models, integrate them cleanly, and monitor them in real-world environments.
What skills do you need to be an AI Engineer?
Technical foundation
Python is non-negotiable (it’s in nearly 100% of AI Engineer job postings). You need at least one major ML framework (TensorFlow or PyTorch), the data science stack (NumPy, Pandas, Scikit-learn), SQL for database work, Git for version control, and basic cloud platform familiarity (AWS, Azure, or GCP).
LLMs and production AI
Employers now explicitly require LLM experience in job descriptions. That means building RAG applications that chat with your data, developing autonomous agents that use tools and make decisions, and implementing prompt engineering strategies that work in production. Job postings list “experience with generative AI” and “multi-agent architectures” as core requirements, not nice-to-haves. You need to work with models like GPT, Claude, and LLaMA — not just call APIs, but understand when and how to apply them. The role of an AI Engineer has moved from building models to building with models.
Neural network architectures and math
Linear algebra, probability, statistics, and calculus aren’t just academic requirements —they’re how you understand what’s happening when you fine-tune a model or debug a training pipeline. Neural network architectures (MLPs, CNNs, RNNs, Transformers) are the foundation for making smart decisions when LLM API calls aren’t enough. You need to know when to use a pretrained BERT model versus GPT, when ResNet18 makes sense for computer vision tasks, and how LSTMs handle sequential data differently than standard RNNs. Employers want engineers who can troubleshoot production issues and adapt when off-the-shelf solutions don’t fit.
Deployment and production skills
You’re expected to build production-ready applications (Streamlit is increasingly standard), work with frameworks like LangChain for agent development and Hugging Face for model integration, implement monitoring systems, and understand LLMOps practices. When job postings say “deploy AI applications that solve real business problems,” they mean applications with real-time data integration, user feedback loops, and proper evaluation metrics — not Jupyter notebooks that run once.
Soft skills
Every job posting we looked at emphasizes communication (explaining technical concepts to non-technical stakeholders), collaboration with cross-functional teams, and the ability to work in fast-paced, ambiguous environments. You’re expected to translate business problems into AI solutions and make technical decisions that non-technical people need to understand and trust. We have lots of free professional skills courses that you can take to improve your career.
Our AI Engineer career path covers all of this. You’ll build neural networks in PyTorch, create RAG applications, develop autonomous agents with tool-calling capabilities, deploy production apps with Streamlit, and work through real-world projects like trip planners, image classification systems, and multi-step AI workflows. Every project maps directly to what employers list in job requirements.
Do you need a degree in AI to become an AI Engineer?
You’ll see “Bachelor’s degree required” in most job postings, but not in AI specifically. The standard requirement is a degree in Computer Science, Data Science, Mathematics, Engineering, or a related field. Since AI engineering is relatively new, most colleges don’t even offer dedicated AI degrees yet — and employers know that.
What matters is the foundation. Hiring managers want to see formal education in a relevant technical field, then evidence you’ve built the AI-specific skills on top of it. That could be courses you took in college, online learning through platforms like Codecademy, or a portfolio of projects that demonstrate hands-on experience. In fact, some postings explicitly state “no demonstrated experience required” if you have the degree, while others consider internship experience or a strong project portfolio as a substitute for formal work experience.
The required 0-2 year experience for entry-level roles means employers expect you to show up with foundational knowledge and the ability to learn quickly — not necessarily years of production AI experience. Build the skills, build the projects, and you’re competitive.
Who is the AI Engineer career path for?
If you’re a developer, data scientist, or even someone who knows how to code a little bit and is looking to advance your career, the new AI Engineer career path is for you. This path is designed for people who already have foundational Python and machine learning familiarity but want to level up into the fast‑growing world of applied AI development — roles where you integrate LLMs, agents, and AI APIs into real products.
The path teaches the exact skills modern AI Engineers use every day: deploying LLMs through APIs, building AI applications with tools like Streamlit, working with agentic systems, applying RAG, monitoring model drift and bias, and understanding enough deep learning to troubleshoot issues without needing “all of calculus and linear algebra,” as Heather says. You’ll learn how to take powerful pre‑trained models and integrate them into real products.
Employers are looking for AI Engineers who can ship AI-powered features. If your goal is to future‑proof your skills, advance your career, and position yourself for emerging AI Engineer job titles, our career path gives you the tools, projects, and applied experience to get there.