How to Become an AI Engineer: Your Complete Guide

If you’re ready to turn curiosity about artificial intelligence (AI) into a rewarding career, good news. Now is an excellent time to get into the field.

Every industry (yes, 100%) increased their AI usage in 2025, including those you might not initially expect such as mining and agriculture. On LinkedIn, AI-related job postings grew 38% from 2020 to 2024, and by the end of the decade, experts say AI will add $19.9 trillion to the global economy.

With AI-fluent talent in high demand, it’s no surprise that compensation is keeping pace. Even entry-level AI engineers can expect to earn between $70,000 to $130,000 a year. Best of all, there’s no PhD required — just a strong portfolio demonstrating required skills applied to real-world projects.

In this guide, you’ll learn if becoming an AI engineer is right for you. And, if it is, how long it will realistically take to make the leap. Let’s dive in.

What is an AI engineer?

An AI engineer builds and trains AI tools that automate processes for businesses and people. These software professionals blend skills from other disciplines, including machine learning (ML), data science, and software engineering, to research, produce, integrate, and maintain AI in real-world systems.

What does an AI engineer do?

AI engineers partner with data scientists, software developers, product managers, and even marketing managers to bring AI products to life. Their North Star is building AI tools that align with user needs as well as business objectives. As a result, they’re hands-on across every stage of product development.

Core responsibilities

AI engineers spend most of their time working with the technical building blocks of AI such as data, algorithms, ML models, and application programming interfaces (APIs) to build AI tools. The rest of their day is spent collaborating with other teams to strengthen their work.

Typical duties

• Designing and implementing AI systems. They use existing models and frameworks, i.e., pre-built components like libraries, datasets, and tools that help accelerate AI development.
  
  
• Building and maintaining data pipelines and preparing datasets. Since training AI on poor quality data can lead to issues like distorted predictions and biases, this is essential to strong AI performance.
  
  
• Training, evaluating, and tuning ML models. AI engineers use ML models to train AI systems to perform specific tasks, and tuning is the process of optimizing a model’s hyperparameters to obtain the best performance.

• Deploying ML models into production and monitoring their performance. Once a model is ready to go live, it’s moved into production and made available to applications and users in real life.

• Collaborating with data scientists, software engineers, and product managers. AI engineers don’t build in silos; they’re in constant contact with other teams to strengthen their solutions.

• Documenting ML models, APIs, and performance metrics. As in any software engineering discipline, quality documentation maintains clarity, accountability, and continuous improvement, all of which is especially important in AI systems.

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Industry applications and use cases

All this can feel a bit abstract when you’re first starting in the field. To get a better picture of what these professionals actually build and how their work creates value for both businesses and people, it can help to see use cases from across different industries. Here are some examples:

• In ecommerce, recommendation systems serve personalized suggestions to shoppers on websites, for example Amazon Personalize, to give customers and easier experience.

• In finance, fraud detection tools like Feedzai help banks and other financial institutions protect their customers and businesses from fraudulent activity and financial crimes.
• Healthcare is an emerging field where AI-powered tools like Aidoc help doctors analyze medical imaging to detect illnesses faster and more accurately.
• In retail, autonomous microstores are popping up as a highly efficient option that’s open 24/7/365. VenHub is one solution making these “robotic retail” shops possible.

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Day-to-day tasks

AI engineer vs. data scientist vs. ML engineer

Data scientists are the researchers of the AI field, providing proof of concept that an AI system is feasible, while ML engineers are the builders who make their proofs work in the real world. AI engineers, on the other hand, create AI systems and ensure they function correctly.

While there’s some overlap in skills between the three roles, they are each unique functions: 

• Data scientists dig through data to find insights, build prediction models, and answer business questions. They work in notebooks and reports and experiment with algorithms to spot patterns. Think: "Can we predict which customers will cancel?"
  
  
• ML engineers take models (sometimes from data scientists) and turn them into systems that work at scale. They focus on performance, training pipelines, and infrastructure. Think: "How do we retrain this model every week with 10 million new data points?”
  
  
• AI engineers put AI into products that customers use. This means connecting models to APIs, handling edge cases, watching performance in the real world, and working with pre-built AI tools. Think: "How do we add smart search to our app and run it 24/7?"

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AI engineer role specializations

The job title “AI engineer” can mean different things to different employers, depending on their size, maturity, and industry. The fact that AI engineering is still an emerging function for many organizations also adds to the variability. To help demonstrate the extent of this diversity, here’s a round up of example AI roles.

Example AI engineering roles

• AIOps (AI operations) engineers automate, optimize, and modernize IT operations using AI, ML, and real-time data analytics.

• Natural language processing (NLP) engineers design applications and algorithms that help computers better understand human language.

• Computer vision engineers develop and implement algorithms and systems that allow computers to interpret visual information.

• Deep learning engineers research, build, and maintain algorithms that power AI and ML systems to learn by example.
  
  
• AI product managers oversee the development and launch of AI products to drive their success for the business and with users.

• AI ethicists monitor the ethical implications of AI from moral, social, environmental, and legal angles and present regulatory solutions. 

• AI research scientists shape advancements in the field of AI through research and analysis of algorithms, methodologies, and performance.
  
  
• MLOps engineers bring ML models to market through development, testing, deployment, and scalability stages with a focus on procedure. 

• Data engineers design and build systems for collecting, storing, and analyzing data at scale for use by data scientists and business analysts.

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AI engineer specialization by industry

By following this path, you can not only specialize by focus, but also by the industry you choose to work in.

• Healthcare AI engineers build diagnostic tools while navigating strict patient privacy laws and medical device regulations so solutions stay compliant with HIPAA.
  
  
• Robotics engineers develop robotic applications, from initial design to writing control software and testing, to ensure robots function correctly.
  
  
• Quantitative AI engineers develop AI systems that detect fraud, assess risk, and scale more swiftly and accurately than a human-only approach.

How to become an AI engineer

Let’s be clear, there’s no one right way to become an AI engineer. With that being said, the majority of people do start from software engineering or data analysis. If you have a non-computer science degree, though, don’t panic; practice and a knockout portfolio can be just as convincing in the eyes of employers.

Key technical skills

1. Programming languages

• Python (absolute requirement): With 70% adoption in AI and ML projects, Python is the most important language to know for AI. Its ability to seamlessly integrate with other technologies allows AI-powered applications to run smoothly in a variety of environments. It also boasts a vast library ecosystem, including ones designed specifically for AI: NumPy, Pandas, SciKit-Learn, Keras, TensorFlow, and PyTorch.
  
  
• SQL (highly important): Production ML systems pull data from databases and use SQL to do it. Examples of actions that SQL enables include: writing efficient queries, seamless joins, and optimizing for performance when working with millions of rows. (If you’d like to start learning SQL for free, consider TripleTen’s free SQL bootcamp designed with beginners in mind).
  
  
• Java (situational): Boasting a large community of developers, Java offers a one-stop solution for implementing ML and AI algorithms and developing applications. It’s also one of the easiest languages to learn and offers a number of AI libraries for natural language processing and neural networks. Java is commonly used by large organizations in back-end systems and for Android apps.
  
  
• C++ (situational): An advanced language that supports methods like functional, procedural, and object-oriented programming, C++ is a fast language that’s well suited for big projects, especially low-latency systems and embedded AI. Additionally, it’s an older language that has a sizable community of users and provides access to a wide range of libraries and frameworks for AI development.
  
  
• R (situational): Originally created for statisticians, R has evolved over time into a favorite tool with AI engineers due to its built-in features for handling data. One thing to know is it uses packages instead of libraries. Basic ones cover data cleaning (dyplr) and data visualization (ggplot, Lattice); advanced ones handle machine learning (Caret, Mlr3) and deep learning (Keras, Torch).

If you’re starting from scratch, learn Python fundamentals, build two or three small AI projects (think chatbot, image classifier, etc.), then you may consider adding a second language.

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2. Machine learning fundamentals

ML algorithms enable AI systems to learn and improve from data without being explicitly programmed and fall into three broad categories: supervised, unsupervised, and self-supervised.

• Supervised learning (learning from labeled examples): This includes regression learning, which predicts numbers such as tomorrow's temperature as well as classification, which categorizes things like spam emails. It’s also important to know when to use what, e.g. linear models for simple patterns, gradient boosting for tabular data, and neural networks for complex patterns.
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• Unsupervised learning (finding patterns without labels): This takes forms such as clustering, a technique that groups similar things together, for example customers that share demographic traits. For this, dimensionality reduction simplifies complex data to visualize patterns. The reality is, though, that most business problems are tackled with labeled data.
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• Deep learning (neural networks with many layers): As part of this, convolutional neural networks (CNNs) excel at recognizing patterns in images (Tesla’s Autopilot uses them to identify stop signs and pedestrians). Recurring neural networks (RNNs) work with time-based data, such as stock price predictions, using an internal memory to identify patterns.

As an entry-level AI engineer, you’ll be expected to know basic ones like linear regression, logical regression, decision trees, random forest, and clustering algorithms.

3. MLOps and deployment

Managing operations at scale for ML projects not only improves model deployment speeds, but also helps AI engineering teams reduce risk and cost. Luckily, there are a variety of tools and frameworks that help.

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What to focus on before you get hired:

• Docker, a tool for packaging your code with all dependencies so it runs the same everywhere each time you deploy.
• AWS Sagemaker, a cloud platform that handles the infrastructure complexity for you during quick deployments.

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What you’ll learn on the job:

• Kubernetes, which manages hundreds of Docker containers running simultaneously so you can handle high traffic at scale.
• Weights & Biases or MLflow, which record every experience you run across model development so you can reproduce what worked.
• Apache Airflow, which schedules tasks to run automatically for when models need retraining with new data.
• FastAPI or Flask, which create web services that let other applications use your model's predictions.

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Also worth mentioning: data preprocessing, the act of cleaning, integrating, and transforming raw data into formats suitable for analysis, ML, or mining is an essential part of the MLOps process. However, this might not necessarily be under the aegis of the AI engineering role at your company.

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4. Cloud platforms

Modern AI runs in the cloud because the computing power required to train models is massive. Most companies rent this power rather than buying their own servers. (For reference, training a large model on high-end GPUs costs anywhere from $50-$500 per run!)

When starting out, it’s a best practice to choose one cloud platform to learn deeply:

• AWS (Amazon Web Services) has 29% market share and is used by many enterprises, which is why most job postings mention AWS. Amazon’s SageMaker is used for ML-specific tasks and Amazon’s EC2 for general computing.
• GCP (Google Cloud Platform) is popular in data-heavy companies and is the best choice if you’ll be working with TensorFlow. Google’s Vertex AI handles the full ML workflow from training to deployment.
• Azure (Microsoft) is common in companies already using Microsoft products and is good for enterprise environments. Microsoft’s Azure ML provides model management tools across the end-to-end lifecycle.

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5. Mathematics

Good news: you need only solid foundations in mathematics to become an AI engineer, not an advanced degree. What actually matters is linear algebra (for understanding matrix operations for neural networks), calculus (for understanding why gradient descent works), and probability/statistics (for interpreting model outputs and detecting statistical significance).

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When you can explain why your model might be overfitting (memorizing training data instead of learning patterns) and suggest solutions like regularization or getting more diverse data, you're positioned well for most AI engineering roles!

Minimum tech stack for your first junior AI role

Essential soft skills

If you're coming from another field, your soft skills may be your biggest advantage. AI engineering isn't just about writing algorithms, it's also about understanding business problems, explaining limitations to executives, and translating business needs into technical requirements. 

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These professional capabilities matter as much as your Python skills:

• Communication. AI engineers need to be able to explain complex topics to non-technical stakeholders across the business, which requires strong speaking and writing capabilities, as well as patience for answering follow-up questions.
  
  
• Collaboration. Working with product, data, engineering, and external vendors requires consistent teamwork and clearly defined parameters for collaboration . Keeping stakeholders in the loop and anticipating roadblocks goes a long way when you’re an AI engineer.
  
  
• Problem-solving and critical thinking. Correctly identifying problems or challenges is just the first stage of AI engineering. You then have to be able to analyze possible solutions and implement the best one (maybe an unexpected one) that saves time and money.
  
  
• Curiosity and willingness to experiment. AI engineers can’t settle for the path of least resistance. They need to constantly ask “Why?” and experiment until they find the strongest solutions for the real-world problems they’re solving.
  
  
• Adaptability. AI is a fast-changing field, which makes keeping up with new tools a must for AI engineers. The prospect of innovation at a rapid pace should be exciting, not overwhelming if you’re considering this career path.
  
  
• Ethical awareness around data and AI impact. Prioritizing ethical considerations in AI development is a critical topic in the field of AI engineering. As such, you want to be fluent in designing tools that don’t harm individuals, society, or the environment.

AI engineer education requirements

As mentioned earlier, you do not need a PhD to become an AI engineer. But you will need a strong portfolio and demonstrated proficiency in necessary skills. Here’s how to get both as a career changer, with different options for your stage of life, budget, and desired timeline.

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Bachelor’s degree

Many AI engineers hold a bachelor’s degree in computer science, software engineering, data science, mathematics, or statistics. Why? Because these programs help build a solid foundation in many of the technical skills we explored above such as programming languages, deep learning, and data preprocessing.

• A master’s degree can add value as you progress in your career, especially if your specialization requires it to advance. As for a PhD? It’s great for going deeper into research or teaching.

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Professional bootcamp

Professional bootcamps are a great option for career changers in particular who already have prior professional experience or even a non-CS degree. These intensive training programs last six to nine months and are designed to help you learn the most relevant and in-demand technologies in AI.

• Another avenue for upskilling and demonstrating your subject matter expertise in AI is earning a professional certification post-bootcamp. Consider ones from AWS, DeepLearning.AI, Microsoft, or Google.

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Self-directed learning

Last, but not least (and definitely the most challenging option), is taking the self-directed approach. This looks like setting your own learning goals, curriculum, and schedule, then sourcing relevant training through books, free and low-cost training platforms, podcasts, YouTube channels, and hands-on projects.

• If you’re considering this path, be sure to give yourself enough time in your weekly schedule to build up community and find mentorship online. You can’t grow in a vacuum.

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AI professional certifications

Regardless of educational path, it’s expected that once AI engineers start their careers, they get certifications from trusted sources to demonstrate their expertise is staying up-to-date. There are many AI-specific ones to explore, but popular ones right now include: IBM AI Engineering Professional Certificate, AWS Certified Machine Learning – Specialty, and TensorFlow Developer Certificate.

Getting your first AI engineer job

Making the leap from job-ready to employed as an AI engineer might feel daunting as you read this guide. But we help people from all backgrounds break into tech and know you can be the next one to land that pivot. The most important thing is to have a plan going in.

Tip #1. Search beyond “Junior AI engineer”

Job titles are inconsistent in tech, especially in AI engineering. So instead of “junior” roles, be sure to also search for:

• "Machine learning engineer" (early-career)
• "Applied scientist" (Amazon's term)
• "Research engineer" (often entry-level at AI companies)
• "Data scientist" on ML/AI teams (sometimes includes engineering)

Be strategic about company size, too. Mid-sized companies (100-1,000 employees) and well-funded startups (Series B+) hire junior talent more readily than FAANG, which can demand 2-3 years experience even for "new grad" roles.

Tip #2: Build a portfolio that demonstrates end-to-end thinking

Recruiters see 1,000 "predicted house prices with linear regression" projects. To differentiate yourself, it’s critical to make good projects great by including a live demo (i.e. in a Hugging Face Space), a GitHub repository, a technical writeup, and performance metrics and failure modes.

Check out our detailed tips on building a strong portfolio here, including real-world examples and stories from TripleTen grads on what helped them stand out.

Tip #3: Master technical interview fundamentals

Technical interviews are different from regular interviews, evaluating you for your hard skills and fluency on different technologies. Practicing the fundamentals before you get in front of a hiring manager is essential to showing off your proficiency in the best light.

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Expect questions on:

• ML concepts (explaining bias-variance tradeoff, or defending when to use random forests vs. neural networks)
• Coding (you can explore and even practice different interview questions on LeetCode rated for medium difficulty)
• System design (for example, having an answer for how you would design a recommendation system for 10 million users)
• Case studies (real-world proof of your work’s impact, e.g. “model accuracy dropped 10% in a week, this is how I debugged…”)

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Practice specifics:

• Explain PyTorch vs. TensorFlow trade-offs (TL;DR, PyTorch dominates research/startups and TensorFlow is more common in production at more established companies)
• Discuss a project failure and what you learned, formatting your answer using the STAR framework (situation, task, action, result)
• Conduct mock interviews with peers you already know, or use an online community like Pramp or interviewing.io to get connected to peers online

Tip #4: Set realistic expectations for your search

Breaking into AI engineering is a numbers game combined with strategic positioning. It’s important to plan for volume in applications, be flexible with job titles, and understand how geography affects your odds. The most important element of a successful search? Mindset.

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Here’s what actually works:

• Applying to 50+ positions before expecting offers
• Accepting your first role might be "MLOps engineer" or "data engineer" on an AI team
• Relocating to a mid-sized city where competition is less fierce for entry-level roles
• Considering adjacent paths (analytics engineer, back-end engineer) to get exposure
• Gaining real-world experience through internships or externships
• Treating your first job as a learning opportunity, not a final destination

Salary expectations and career progression

The truth is that geography dramatically affects pay when starting out in AI engineering. But once you’re in the career track, it’s typical to advance quickly across both responsibilities and compensation. Here’s a closer look at what you can make at each stage.

Entry-level compensation (0-2 years)

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Other factors influencing pay as an entry-level engineer:

• Education. PhDs from top programs command $150K+, while bootcamp grads typically start $80K-$100K.
• Portfolio quality. A strong portfolio demonstrably increases your earning potential. For example, having models in production, meaningful open-source contributions, or technical writing indicates you are a new hire that will contribute immediately.

Career progression

Salary growth in AI is faster than most tech roles, with many engineers seeing significant salary increases within their first two years by developing deployment skills, learning MLOps, or switching companies (a common strategy in tech for salary jumps).

*Salary data sourced from Glassdoor, DataCamp, and CBTNuggets

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FAQ

Do I need a PhD to become an AI engineer?

You don’t need a PhD to become an AI engineer. PhDs help for research roles at labs (Google DeepMind, OpenAI, Meta FAIR), but most industry positions prioritize practical skills. Many successful AI engineers have a bachelor's degree and progress with real-world experience and professional certifications. PhD holders can actually be overqualified for implementation-focused roles!

How long does it take to become an AI engineer?

It can take between six and 12 months of focused study to become an AI engineer. As a complete beginner, you’ll need to spend that time practicing daily, building real projects, applying for roles, and interviewing with companies. We can’t stress enough how important building up your real-world experience is versus just watching tutorials.

What programming language should I learn first for AI?

You should learn Python first due to its importance in AI engineering. Gain skills by doing, then fill in your mathematical gaps as you progress in your career. For example, understanding why gradient descent works matters a lot but you'll learn why better by implementing it than by taking courses.

Can I become an AI engineer without a computer science degree?

Yes, you can become an AI engineer without a computer science degree. The field values skills over credentials. However, you'll need to learn computer science fundamentals independently, such as data structures, algorithms, software design, and version control, before you begin looking for jobs. As mentioned throughout this article, your portfolio will become even more critical to getting hired without a computer science degree.

What's the difference between an AI engineer and a data scientist?

The difference between an AI engineer and a data scientist is one of focus. AI engineers prioritize production: scalable code, deployment, monitoring, system reliability. Data scientists emphasize exploration: hypothesis testing, statistical analysis, prototyping. AI engineers' code runs 24/7 serving users; data scientists' code often lives in notebooks. While the two roles are complementary, they work on different aspects of data-driven innovation.

How much can I earn as an entry-level AI engineer?

As an entry-level AI engineer, you can expect to earn $70,000-$130,000 annually in the US, varying significantly by location and company. For example, San Francisco, New York, and Seattle all push toward $100,000-$120,000+, while smaller markets start closer to $70,000-$90,000. But living in lower cost areas can make that lower base salary stretch a lot farther.

What are the biggest challenges new AI engineers face?

Software engineering practices are hands down the biggest challenges facing new AI engineers. If you're coming from data science or academia, you may lack experience with Git, code review, testing, CI/CD, and API design. It’s important to spend extra time on these fundamentals as a career changer to help your applications stand out with employers.

Do I need advanced mathematics for AI engineering?

You don’t need to know advance mathematics for AI engineering. You just need solid foundations. If you understand matrix multiplication, derivatives, and basic probability, you're positioned well. Deeper math helps for research roles but isn't necessary for 90% of AI engineering positions. So master the fundamentals and attain fluency in things like linear algebra, calculus, and statistics/probability to set yourself up for success.

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