What Does an AI Engineer Do, and How Can You Become One in 2025?

Turing College -

Artificial intelligence (AI) is moving fast, creating a strong demand for skilled AI engineers. This role blends software development with practical AI application, turning research breakthroughs into real products. In this article, we’ll explore the role of an AI engineer, how it differs from other AI and machine learning roles, and what it takes to launch a career in this fast-growing field.

What Is an AI Engineer?

Artificial intelligence (AI) engineers are specialized professionals who use their knowledge of artificial intelligence and machine learning to build practical tools. They don’t train models from scratch or write research papers. Instead, they take what the researchers build and make them work in production.

Think of it as the middle ground between software engineering and AI research. On one side, you have developers shipping features, and on the other, researchers pushing the limits of what models can do. AI engineers connect the two.

They’re not data scientists, ML engineers, or research scientists. And they’re not just frontend or backend developers either. They’re product-focused builders who know how to ship quickly using whatever’s cutting-edge and stable enough to scale.

As Andrej Karpathy, co-founder of OpenAI, pointed out,  there will likely be far more AI engineers than ML engineers — and many will never need to train a model at all. That’s the job: not inventing the tech, but applying it where it counts.

The demand speaks for itself. According to PwC, roles requiring specialized AI skills are growing 3.5 times faster than any other job category. Companies aren’t waiting for custom models — they need people who can build with the tools that already exist. That’s where AI engineers come in.

What Does an AI Engineer Do?

AI engineers play a crucial role in implementing AI — they turn cutting-edge models into real-world features. It’s a hands-on, technical role focused on applying AI models rather than inventing them. They write code, integrate AI tools, and build and implement AI applications using models created by researchers and data scientists. 

Here are some specific responsibilities of an AI engineer:

  • Build and manage the infrastructure needed for AI development and production
  • Transform machine learning models into APIs and tools others can use
  • Automate AI workflows to support data science and engineering teams
  • Run statistical analyses that guide product or business decisions
  • Develop AI models using machine learning algorithms and deep learning neural networks
  • Work with product managers and stakeholders to turn ideas into prototypes
  • Collaborate across teams to scale AI adoption and improve best practices

Why the Role of AI Engineer Is Growing

There are two sides. Big, established companies want to adopt AI for their products, but most are still figuring out how. At the same time, a wave of startups is launching with AI at their core. For those new companies, the success of the product often depends entirely on what the AI engineer can build.

In both cases, the difference between a good AI engineer and a great one is massive. Someone who can ship fast, build what others can’t, and keep up with the pace of change is the person who can shape a product, a company, or an entire market.

How AI Is Transforming Everyday Products

AI is already changing how we use familiar tools, and the shift is happening fast:

  • Notion. What started as a simple note-taking app now relies on AI to summarize content, answer questions, and speed up workflows. It changes how people interact with information.
  • Spendesk. Spendesk uses large language models to automate invoice processing. Instead of manually entering details, users can email an invoice and let the system extract the key data automatically. It cuts down on busywork and reduces human error — exactly the kind of value AI should bring.
  • Revolut. Revolut has added a robo-advisor to guide users through investing. Whether or not it qualifies as “real AI” is debatable, but that’s not the point. The point is: companies are under pressure to experiment with AI, even if the use cases aren’t always clear yet.

AI-Powered Products Built Entirely with Artificial Intelligence

Some startups aren’t just using AI — they are AI.

In mental health, for example, AI therapists are already a reality. These apps offer tailored advice, track progress, and provide emotional support, powered entirely by large language models.

Greenlite is tackling a very different problem: financial compliance. Their platform uses AI and machine learning technology to automate the kind of repetitive, high-stakes work that once required full teams. Their pitch — “The Trusted AI Platform for Financial Crime” — makes the use case clear: this is AI doing high-impact work.

Then there’s Perplexity — part search engine, part AI research lab. It combines existing models with ones they’ve built themselves. It’s one of the few startups pushing the edge of what AI can do, while still delivering something immediately useful to end users.

In all these cases, AI isn’t a feature. It is the product. And building products like these requires engineers who can turn raw models into something people actually use.

AI Engineers in Demand: The Stats

If you’re wondering whether this role is just one of short-term AI trends, the numbers say otherwise.

Job postings for skilled AI engineers are growing fast — so fast, they’ve already caught up with demand for machine learning engineers. And according to Y Combinator, over 70% of startups accepted into its 2024 summer batch had AI at the core of their product. 

Companies across every sector are racing to hire people who can turn AI into real, usable features. If you have the skills, a career in AI is one of the most opportunity-rich roles in tech today. 

What Makes a Successful AI Engineer?

Successful AI engineers stay curious. They follow what’s new, test what’s possible, and talk to others doing the same. The space moves too fast to go it alone. The best people learn from peers rather than just documentation.

AI engineers must also think like product people. AI features are unpredictable. You’re not always working with clear requirements or predictable outcomes. You’re experimenting. You’re making judgment calls on how a feature should behave and what “good enough” looks like. You need to care about the product you’re building and the impact it has on the people using it. If you do, the work is both exciting and meaningful.

Skills Required to Become an AI Engineer

Becoming an AI engineer takes more than just technical know-how. It takes the ability to build, communicate, and ship. The role sits at the intersection of deep tech and real-world impact, so the required skills reflect that mix.

Technical Skills

Technical skills are at the heart of AI engineering. They’re essential for building, deploying, and refining AI models. Here’s what you need:

  • Programming skills. Many of the tools that AI engineers use are powered by Python or Java. 
  • Big data technologies. AI is data-hungry, and dealing with that data efficiently matters. You'll have to handle large datasets using tools like Spark, Hadoop, or MongoDB. 
  • Algorithms and frameworks. Knowledge of machine learning algorithms, deep learning neural networks, and the frameworks that support these is important for developing efficient AI systems.

People Skills

While technical expertise is critical, people skills are equally important for success as an AI engineer. You’ll be part of cross-functional teams, building tools for real users, and these soft skills are essential for getting things done:

  • Communication skills. AI engineers need to communicate clearly, especially when explaining technical trade-offs to non-technical stakeholders.
  • Problem-solving abilities. You’ll need to think critically and creatively, building AI solutions that solve real problems rather than optimize metrics.
  • Domain expertise. Understanding the industry context helps you tailor your AI models to create meaningful impact.
  • Business intelligence. It's not enough to build an AI model that works — you need one that delivers business outcomes.

Jobs in AI are competitive, but but if you’ve got the right mix of skills and the mindset to keep learning, you can launch a successful career as an AI engineer.

Prepare for an AI Engineering Career with Turing College

If artificial intelligence is the career path for you, but you don’t have a computer science degree or the time for a four-year program, you still have options. This year, Turing College launched a dedicated AI Engineering program, which helps you discover the tools and skills needed to become an AI engineer.

Key Program Features

  • Who it’s for: You’ll need prior coding experience — either a year in a professional role or completion of a coding-heavy program (like data science, web development, or advanced analytics).
  • Languages supported: You can choose between Python and JavaScript.
  • Program length: ~3.5 months (self-paced)
  • Effort required: ~10 hours/week
  • Format: Fully remote, with access to expert mentors, team leads, and a community-driven learning environment.

What You’ll Learn

The program is divided into four sprints:

  1. Foundations of AI Engineering
    • Understanding different types of models
    • Prompt engineering
    • AI product development lifecycle
  2. Tools for AI Product Development
    • LangChain
    • RAG (retrieval augmented generation)
    • Google Vertex AI
    • Gradio and other developer tools
  3. Building AI Agents
    • Creating systems capable of long-term, multi-step tasks
    • Building beyond simple chatbots
  4. Capstone Project
    • Design and develop a real-world AI feature
    • Receive expert feedback and peer reviews

The program emphasizes product-thinking and encourages experimentation, as AI projects often don't follow traditional software development workflows.

Who You’ll Learn From

The curriculum is developed and reviewed in collaboration with AI engineers and data scientists from:

  • IBM
  • SAP (Silicon Valley)
  • App Makers (known for viral generative AI content)
  • Google Cloud (a content partner offering platform credits and review)

These experts are involved in mentoring, reviewing projects, and ensuring the curriculum stays aligned with the latest developments in the field of AI engineering.

What Makes This Program Unique?

AI changes fast, and so does this program. Turing College constantly updates its content to keep pace with new tools, models, and breakthroughs. You won’t be learning last year’s tech.

Unlike many AI courses that rely on passive video content and quizzes, Turing College's program is community-driven. You’ll work closely with mentors, content leads, and peers through regular standups, project reviews, and live sessions. You'll actually build AI tools with guidance from people who do this work every day.

New to Coding? 

Turing College also offers a separate program: Software & AI Engineering, designed for complete beginners. It teaches you how to code from scratch, then builds toward the same AI skills, so you can start wherever you are and still end up job-ready.

FAQ

Can you really learn AI engineering in 3.5 months?

Yes, if you already know how to code. This program skips the basics and focuses on the AI-specific skills you actually need. The role is still new, and continuous learning is a key part of a career in AI engineering.

Will AI engineering become part of full-stack development?

Artificial intelligence showing up in the daily toolkit of many developers. But turning models into reliable features takes a different mindset and skill set. That’s why AI engineering is growing as its own, in-demand role.

What is the future of AI engineering?

As AI technology matures, expect even more specialization. Here are a few roles already taking shape:

  • AI ethics officer. A new role ensuring AI systems are built and used responsibly.
  • Quantum AI specialist. This role will explore the overlap between AI and quantum computing.
  • AI infrastructure engineer. This job category will specialize in building and maintaining the infrastructure for AI development and production.
Explore AI Engineering program