How to Become an AI Engineer

How to Become an Artificial Intelligence Engineer?

Now that you know what an AI engineer does, here are the steps you can take to start your career in artificial intelligence engineering:

Step 1: Obtain a Secondary School Diploma

Start by completing high school with a strong focus on scientific subjects like mathematics, physics, and chemistry. A solid foundation in these areas is crucial, and including statistics will be an added advantage. This will set the stage for your future education in AI and related fields.

Step 2: Complete a Bachelor's Degree

Next, enroll in a Bachelor's program, preferably in fields like Computer Science, Information Technology, or Engineering. These programs typically last three to four years and provide a strong base in programming, algorithms, and data structures. To get into reputed institutions such as IITs, the NITs, and the IIITs, you will need to do well in entrance exams such as JEE. During your degree, you can choose to specialize in artificial intelligence, machine learning or data science.

Step 3: Pursue a Master's Degree (Optional but Recommended)

Although a Bachelor's degree is enough to start working in AI engineering, pursuing a Master's degree in AI or a related field can give you an edge. A Master's program offers advanced knowledge and technical skills, making you more competitive in the job market. 

Step 4: Earn Certifications

Certificates are a way to improve your skills and keep up with new developments. There are plenty of online courses and diploma programs in AI, machine learning and data science that are affordable and offer flexibility to your learning. Good certifications from reliable platforms like Simplilearn can make your resume stronger and reflect your commitment to learning.

Step 5: Acquire Practical Experience

Hands-on experience is essential for becoming proficient in AI. Engage in projects, internships, and hackathons to apply your theoretical knowledge to real-world problems. Working on open-source AI projects or contributing to GitHub repositories can also help you build a strong portfolio. In addition, consider enrolling in bootcamps or online programs that offer practical projects and mentorship from industry professionals. This experience will be invaluable as you tackle complex AI challenges in your future career.

Next up, let’s look at some real-world examples of AI engineering being applied to solve practical problems and improve various industries.

AI Engineering Examples in the Real World

• AI-Powered Antibody Discovery

AION Labs, in partnership with several big pharmaceutical companies, such as AstraZeneca and Pfizer, has launched DenovAI. DenovAI is an AI-based platform that can de novo discover antibodies. This technology has the potential to save money and speed up the process of developing a new antibody and is a major advancement in drug development.

• AI-Driven Building Energy Efficiency

BrainBox AI implemented its AI system to optimize HVAC operations in commercial buildings, resulting in significant energy savings and a reduction in CO₂ emissions. The AI-driven system improves energy efficiency by adjusting HVAC settings based on real-time data.

• AI in Defense Systems

Lockheed Martin is integrating AI into its defense systems, including developing autopilot systems for uncrewed aerial vehicles (UAVs). These AI-driven solutions aim to enhance mission capabilities and reduce development time and costs. ​

• AI-Enhanced Chip Design

Google DeepMind's AlphaChip employs reinforcement learning to guide chip placement, reducing the time required for chip layout from weeks to hours. This AI-assisted design process has been utilized in the development of Tensor Processing Units (TPUs). ​

• AI for Public Safety in UAV Networks

Generative AI models are being used to optimize resource management in UAV-assisted Internet of Things (IoT) networks. These AI systems enhance real-time decision-making, particularly in public safety scenarios, by improving training datasets and operational efficiency.

As automation and AI adoption continue to rise, AI engineers will remain indispensable, making it one of the most future-proof professions in tech. Learn AI Engineering to secure your tomorrow! 🎯

Responsibilities of an AI Engineer

If you're thinking about becoming an AI engineer, you’ll have a wide range of responsibilities, including but not limited to:

• Developing AI Models from Scratch

You will primarily be building AI models from scratch. You will use multiple algorithms, including logistic regression, random forests, and even deep learning, to build models that fit a specific problem. Basically, you will identify business problems and build models that can address those problems.

• Converting Models into APIs

Once you have a model ready, the next step is to make it usable by other systems in the organization. This is where you’ll convert your AI models into APIs (Application Programming Interfaces). APIs let different applications communicate with your model, which means the model can be used in real-time to make decisions or automate processes across the company.

• Building Data Ingestion and Transformation Infrastructure

For AI models to work, they need to be fed with clean, well-organized data. You’ll be responsible for creating the systems that collect, clean, and prepare data for the models.

• Automating Data Science Infrastructure

As part of your role, you’ll also be focused on making the work of the data science team more efficient. By automating key infrastructure tasks, like setting up environments or data processing workflows, you help ensure that the team can focus more on analyzing data and less on repetitive tasks.

• Managing AI Development and Product Infrastructure

You will also manage the infrastructure on which AI gets built. You will be responsible for selecting the right tools, setting up platforms and making sure the entire AI pipeline is working optimally and efficiently.

• Collaborating Across Teams

AI doesn’t work in isolation. You will collaborate closely with product managers, data scientists and other stakeholders. You’ll have to communicate yourself well and translate complex technical concepts into understandable terms.

Skills Required to Become an AI Engineer

Becoming an AI engineer isn’t just about having the right technical knowledge—it’s also about having the ability to work well with others and effectively communicate complex ideas. Let’s take a closer look at the key skills required to thrive in this role.

• 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

Knowing languages like Python, R, or Java is essential because they allow you to build, train, and deploy AI models effectively.

• Mathematics & Statistics

A strong foundation in linear algebra, probability, and statistics is vital to understanding how machine learning models work and perform.

• Big Data Technologies

Working with huge volumes of data is common in AI, so tools like Spark, Hadoop, or MongoDB help manage and process data efficiently.

• Algorithms & Frameworks

Knowledge of machine learning and deep learning algorithms and the frameworks that support these algorithms is important for developing scalable and performant AI systems.

• People Skills

While technical expertise is critical, people skills are equally important for success as an AI engineer. Whether you’re collaborating with colleagues or explaining your work to stakeholders, these soft skills are essential for getting things done.

• Communication Skills

AI engineers need to explain technical ideas clearly to both technical teams and non-technical stakeholders for smoother collaboration and decision-making.

• Problem-Solving Abilities

Solving real-world challenges is at the core of AI work, so creative and analytical thinking helps you develop reliable AI solutions.

• Domain Expertise

Understanding the business or industry helps you tailor your AI models to solve relevant problems and create meaningful impact.

• Business Intelligence

Connecting technical solutions to business outcomes is what turns AI models into tools that actually help companies grow and compete.

• Critical Thinking Ability

Evaluating data and making smart decisions quickly is crucial when refining models or responding to unexpected AI behavior.

AI Engineer Salary

AI engineer’s salaries vary based on location, experience, and company. Here's a look at what AI engineers earn:

• United States

In the U.S., AI engineers make around $103,500 per year on average. If you’re working in big tech cities like San Francisco or New York, that number can go even higher.

• India

In India, engineers in this space earn an average of ₹9,00,000 per year, with higher salaries common in tech hubs like Bengaluru and Hyderabad.

• Around the World

Globally, the demand for skilled engineers is growing across industries. This has led to competitive salaries, with companies offering attractive packages to bring in top talent.

Top AI Career Choices

While the salary of an AI engineer reflects the value of their expertise, let's take a closer look at the top job roles in the AI field that offer promising career growth.

1. AI Developer 

An AI developer works closely with electrical engineers and develops software to create artificially intelligent robots.

2. AI Architect 

AI architects work closely with clients to provide constructive business and system integration services. They also create and maintain the entire architecture.

3. Machine Learning Engineer 

Machine learning engineers build predictive models using vast volumes of data. They have in-depth knowledge of machine learning algorithms, deep learning algorithms, and deep learning frameworks.

4. Data Scientists 

Data scientists collect, clean, analyze, and interpret large and complex datasets by leveraging both machine learning and predictive analytics.

5. Business Intelligence Developer 

BI developers are  responsible for designing, modeling, and analyzing complex data to identify business and market trends.

Conclusion

AI engineering is a growing field with plenty of opportunities across different industries. As more companies embrace AI, the need for engineering talent continues to expand, making AI engineering a stable career option. Whether you're just starting or looking to build on your existing skills, AI engineering offers many ways to make a real impact.

If you're looking to dive deeper into AI or advance your career, consider the Artificial Intelligence Engineer program by Simplilearn. It offers practical experience and a strong foundation to help you succeed in this field.

FAQs

1. Is AI engineering a good career?

Yes, AI engineering is a rapidly growing and in-demand career field with a promising future. As organizations continue to adopt AI technologies, the demand for skilled AI engineers is only expected to increase. AI engineers can work in various industries and domains, such as healthcare, finance, manufacturing, and more, with career growth and development opportunities.

2. Is AI engineering hard?

AI engineering can be challenging, but regular learning and hands-on practice makes it a manageable and rewarding career path.

3. Can You Become an AI Engineer Without a Degree?

While it's possible to gain the necessary skills through self-learning, bootcamps, and certifications, becoming a successful AI engineer typically requires a strong foundation in programming, mathematics, and data science, which is usually acquired through a formal degree. Without a degree, you might face more challenges in entering the field and advancing your career.

4. How long does it take to become an AI engineer?

The time it takes to become an AI engineer depends on several factors, such as your current level of knowledge, experience, and the learning path you choose. However, on average, it may take around 6 to 12 months to gain the necessary skills and knowledge to become an AI engineer. This can vary depending on the intensity of the learning program and the amount of time you devote to it.