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How To Become an Artificial Intelligence (AI) Engineer

Explore the path to becoming an AI engineer and learn the essential skills and steps to launch your career.

How To Become an Artificial Intelligence (AI) Engineer
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Artificial intelligence engineers are responsible for developing and implementing AI systems. They create models and complex algorithms that define how AI works. These professionals also train models using large data sets to help AI learn and make more accurate predictions.

The Bureau of Labor Statistics (BLS) predicts that employment in the computer and information research scientists occupation—a field that includes AI engineering—will grow by 26% between 2023 and 2033. This is good news because there will be plenty of roles to fill.

The projected growth also shows how valuable AI engineers are and is indicative of the expansive field of AI. Whether in manufacturing, marketing, finance, or health care applications, AI professionals can help organizations harness the power of artificial intelligence and use it to boost productivity and creativity.

If you want to be an artificial intelligence engineer, you're in the right place. We'll explain what an artificial intelligence engineer is and share how to start a career in AI engineering.

What is an artificial intelligence engineer?

An artificial intelligence engineer builds AI models and machine learning algorithms and trains them using large data sets to learn patterns and make predictions. AI models may not work exactly like a human brain, but they can exhibit intelligent behavior in specific tasks.

Other roles and responsibilities AI engineers have include:

  • Create application program interfaces (APIs) that other developers can import and use in their applications using machine learning models.
  • Manage infrastructure related to AI development and maintenance, including setting up computing environments and data storage systems and ensuring the scalability and efficiency of AI systems.
  • Train other professionals on how to use AI, including providing guidance on best practices and creating documentation and concise prompts that return helpful results.
  • Conduct statistical analysis on big data and gain valuable insights for decision-making, including deploying statistical techniques and data visualization methods to better understand the data and uncover patterns.
  • Develop deep learning algorithms like neural networks to boost the performance of AI applications. Deep learning algorithms are great at image and speech recognition, natural language processing (NLP), and recommendation programs.

An artificial intelligence engineer must know how to code using programming languages like Python, C/C++, and R. A solid understanding of AI technologies and models like Naive Bayes, Hidden Markov, and Gaussian mixture is also beneficial.

A successful AI engineer should possess strong analytical skills since the role regularly involves handling large data sets. Other vital skills and competencies an AI engineer need include:

  • Excellent communication skills (both verbal and written)
  • Critical thinking skills
  • Leadership skills
  • Problem-solving skills
  • Knowledge of statistics, calculus, and probability
  • Attention to detail‍
  • The ability to communicate with key stakeholders

The role of an AI engineer is different from a machine learning engineer. An ML engineer is tasked with building models for analyzing a large amount of data. They use this data to establish predictions or add automation to decision-making.

How to start a career in AI engineering

From laying a solid foundation to pursuing the right courses, we'll walk you through the steps to becoming an AI engineer.

Gain a strong foundation

Lay a strong foundation for your AI career by pursuing a bachelor's or master's degree in computer science, mathematics, information technology (IT), or another related field. These degrees typically cover important concepts and skills, such as programming languages, algorithms, data structures, and mathematical foundations.

Familiarity with data structures and algorithms is also important for handling large data sets, and linear algebra can help you understand the mathematical foundations of machine learning algorithms (e.g., matrix operations and vector spaces).

Finally, regression analysis and statistics allow you to assess model accuracy, analyze data patterns, and make predictions based on the data.

Earning a degree can also sharpen your existing skills in problem-solving, critical thinking, communication, and analysis, which are essential in AI engineering.

Learn AI fundamentals

Mastering AI fundamentals gives you the confidence and ability to understand more advanced concepts you'll meet later in your career or learning journey.

As you venture into artificial intelligence, ensure you have a good grasp of deep learning, machine learning, and NLP concepts. Deep learning is a subset of machine learning that focuses on training artificial neural networks to learn and make predictions.

Machine learning involves developing algorithms that allow systems to learn from data and make predictions, while NLP deals with the interaction between computers and human languages.

Artificial intelligence models also rely on numerous libraries to function. As a result, pay attention to popular AI algorithm frameworks and tools like scikit-learn, PyTorch, and TensorFlow.

Develop programming skills

An artificial intelligence engineering role requires a wide range of technical skills. Among the most noticeable competencies is knowing how to code using programming languages like Python, C/C++, and Java.

The AI field commonly uses Python due to its extensive libraries and frameworks for machine learning and data analysis. Languages like C/C++ and Java can benefit specific AI applications.

You may not immediately understand certain concepts while working on your coding skills. This is a normal part of the learning process. The key is to be persistent. Keep practicing, working on projects, and expanding your knowledge.

Improve your programming skills by joining tech communities and forums (like GitHub and Stack Overflow) to meet other like-minded individuals. These people can help you understand different concepts and empower you to be a good programmer.

Engage in hands-on projects

Although you've learned a new programming language, some things may not make immediate sense. For instance, you may not know where certain classes or functions fit in an application, when to import AI libraries, or which AI framework to use in your project.

This is where hands-on AI projects come in, allowing you to apply your knowledge in a practical setting. In the process, you'll learn what fits where, what different messages mean, and more.

Project-based learning—such as building a chatbot or a spam filter—can improve your critical thinking and problem-solving abilities. You'll become more confident by solving real-world problems and can apply these skills in your future employment.

Apart from building personal projects, you can grow your skills by collaborating with peers on Kaggle Competitions or contributing to open-source initiatives.

Learn from online resources

Boost your learning by taking advantage of online courses and tutorials. Online learning is cost-effective and accessible, meaning you can interact with resources from anywhere with an internet connection. You can also enjoy the flexibility that online learning offers, including the ability to read whenever you want.

Platforms like Coursera, Udacity, and edX allow you to take specialized courses on artificial intelligence. They cover various AI topics, including machine learning, deep learning, and NLP, and feature AI industry greats, including Coursera co-founder Andrew Ng.

Apart from the platforms mentioned, numerous other online resources are available for learning AI. These include YouTube tutorials, AI-focused blogs, and academic publications. Exploring multiple resources can provide a broader perspective and help you find the learning materials that best suit your needs and learning style.

You can also keep tabs on what's happening in artificial intelligence and other related industries through the Upwork Resource Center.

Build a portfolio

Once you've gained the right knowledge and sharpened your skills, it's time to build a portfolio that showcases what you can do for potential employers. Since other qualified candidates may have their eyes on your desired job, your portfolio should feature the right content to show recruiters what you can bring to the table.

A good portfolio should highlight your skills and some of the most important work you've completed. Including AI projects you've worked on and their outcomes in your portfolio can make you stand out from other applicants and convince recruiters you have the skills and knowledge they seek.

Your AI portfolio should also state the programming languages you're proficient in, libraries you're familiar with, and any testimonials from mentors and other experts.

Recruiters will use your portfolio to check if you have good hands-on experience with software development, data engineering, programming, or data analytics and determine if you're the best candidate for them.

Seek practical experience

Gaining practical experience is a good way to enhance your knowledge and skills using different AI technologies, tools, and workflows. Apart from the technical aspect, you also learn valuable soft skills, such as communication, problem-solving, critical thinking, teamwork, time management, and discipline.

For practical experience, look for internships or entry-level positions in various AI-related roles. These positions let you work with AI teams and build real-world projects, gaining exposure to industry practices and workflows. You also get to meet mentors who can lead you down the correct path.

Stay updated on the latest technology

The technology scene is changing rapidly. As an AI engineer, keeping up with emerging technologies and trends is important. AI frameworks are released regularly, so continuous learning is vital to stay updated with the latest research and industry developments.

Start engaging with the AI community to build a professional network, gain valuable business insights, and find job opportunities. Attending AI-related conferences is also a good way to gain exposure to new concepts, trends, and plans. Conferences often feature keynote speakers, workshops, and technical sessions.

Participating in forums, online communities, and discussion boards can also help you learn new things and engage with peers and experts.

Understanding the field of AI and its broader applications will help you succeed. The more you learn, the more connected you'll be to the latest updates.

Network and collaborate

Networking allows you to create a support system of other like-minded people to turn to for help on your projects, career advice, and other favors. You can also collaborate on AI projects to expand your knowledge and build relationships.

Connect with data scientists, product managers, software engineers, research analysts, and other AI professionals through networking events, conferences, and online platforms such as LinkedIn.

Pursue advanced education

Pursuing advanced education like a master's degree or doctorate allows you to specialize in specific subsections of AI, such as natural language processing, computer vision, machine learning, robotics, or related fields. You can develop deep expertise and knowledge in a particular domain, making you valuable for specific roles or industries that require specialized AI skills. Universities such as Stanford also offer certification programs in AI.

Advancements in education can give you a complete understanding of AI theories, methodologies, and advanced concepts, making you more effective as an AI engineer. You can also pursue certifications like the Certified Artificial Intelligence Engineer (CAIE) program to give you an edge over other candidates during job interviews.

Apply for AI engineering roles

Once you've completed the above steps and acquired the right skill sets, it's time to launch your career by applying for various AI engineering roles.

Prepare a compelling resume that highlights your AI skills and projects. Rather than reusing templates, customize each resume and cover letter to fit a specific job description.

Your tone should be enthusiastic but professional. Remember to be clear and concise, focusing on what you're bringing to the table and leaving out the fluff. We have some helpful tips for writing a great resume that stands out.

You can apply for AI engineering positions in different companies and research institutions. But if you value more freedom and flexibility, consider doing freelance AI engineering on Upwork, the world's work marketplace.

AI engineering FAQ

AI engineering is a rewarding and fulfilling career requiring dedication and commitment. We answer some common questions about AI engineering.

What is the average AI engineer salary?

According to the Bureau of Labor Statistics (BLS), AI engineers have a median salary of $145,080 per year. Keep in mind this does not necessarily reflect pay for beginners in AI engineering.

How long does it take to become an artificial intelligence engineer?

The time it takes to become an artificial intelligence engineer depends on various factors, including your current skills, knowledge, and desired learning path. Most AI certification courses take between six and 12 months. But if you pursue a bachelor's degree in computer science or a related field, it may take you about four years to graduate as an AI engineer.

Is a career in AI engineering difficult?

A career in AI engineering can be difficult, especially if you lack a strong foundation. This could indicate you need more hard skills relating to engineering, statistics, mathematics, and computers.

On the other hand, if you have a strong interest in and aptitude for computer studies, you may find AI engineering no more difficult than your other mathematics and technical foundational training.

With the proper training and a good level of preparedness, you can find an artificial intelligence career attainable and fulfilling. You can also have an easier time by joining tech communities, participating in forums and open-source events, and collaborating with others.

What degrees are required for an AI engineer?

An AI engineer typically requires a bachelor's degree in computer science, data science, statistics, or computer engineering.

Depending on the organization, you may also need a master's degree with a specialization in areas like computer vision, robotics, or natural language processing. Certain companies may also require proof of completing various artificial intelligence courses and certification programs.

What is the best way to get into AI engineering?

You can become an artificial intelligence engineer by attending online bootcamps and earning the proper certifications. However, the best way to kick-start your AI engineering career is to enroll in a university and get a bachelor's degree in computer science, mathematics, data science, or any other related field.

Since educational institutions have well-established curricula, you're in a better position to master AI fundamentals. Plus, you're connected to your peers from the start, which is more convenient for teamwork and collaboration.

Find artificial intelligence jobs on Upwork

Artificial intelligence is becoming increasingly popular due to its ability to enhance creativity and productivity. As a result, the demand for AI engineers is at an all-time high—making this a perfect time for you to kick-start your career.

Take advantage of online resources in your journey to becoming an AI engineer. You can also meet mentors on social platforms and tech communities who can help shape your career path.

Once you've gained the right skills and knowledge, start your AI engineer job search on Upwork. You can find work and meet prospective clients on the world's work marketplace. Get started today.

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How To Become an Artificial Intelligence (AI) Engineer
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