Learn How to Be an Artificial Intelligence (AI) Engineer Fast

Summary: Becoming an artificial intelligence engineer is easier than ever with the right approach. Start by learning the basics, earning certifications, gaining hands-on experience, and applying for entry-level roles. Online resources and platforms like Pickl.AI make it accessible to anyone ready to dive into this fast-growing tech field.

Introduction

An Artificial Intelligence (AI) Engineer is a person who builds smart software that can think, learn, and make decisions, just like a human. As AI becomes a part of our everyday lives, from smart assistants like Siri and Alexa to driverless cars and online shopping recommendations, the demand for skilled AI Engineers is growing fast.

In fact, it’s expected that almost one million people will be working in AI by 2025. If you’re curious about this exciting career path and want to know how to become an AI Engineer quickly, even if you don’t have a technical background, this guide is for you.

Key Takeaways

• AI engineers design smart systems that mimic human decision-making.
• You don’t need a tech degree to start—online courses and certifications work.
• Key skills include Python, machine learning, and data analysis.
• Real-world projects and certifications boost your job readiness.
• AI and data science are deeply connected—learning both enhances career potential.

What Is Artificial Intelligence (AI)?

Artificial Intelligence is a branch of computer science focusing on making machines smart. These machines can then do tasks that usually require human thinking, like recognising faces, understanding language, or predicting trends.

AI is all around us. For example:

• Amazon uses AI to suggest what you might want to buy next.
• Self-driving cars use AI to understand road signs and traffic.
• Chatbots use AI to answer customer service questions instantly.

Simply put, AI helps computers “think” and make data-based decisions.

What Does an Artificial Intelligence Engineer Do?

AI Engineers create the brain behind these smart machines. Here’s what their job usually includes:

• Designing AI systems that solve real-world problems.
• Writing code to teach machines how to think.
• Working with teams to build new AI products like facial recognition or voice assistants.
• Testing AI models to ensure they work correctly.
  

Some engineers focus on specific areas like machine learning, robotics, or computer vision (helping machines see and understand images).

The Fast-Track Roadmap to Becoming an AI Engineer

Let’s break down how you can become an AI Engineer step-by-step.

Start With the Right Education

You don’t need a PhD to get started. Most AI Engineers begin with a bachelor’s degree in a related field like:

• Computer Science
• Information Technology
• Software Engineering

But if you don’t have this background, don’t worry. Today, many online courses and certifications can help you learn from scratch.

Popular Online Courses Include:

• Coursera’s “AI for Everyone”
• Google’s “Machine Learning Crash Course”
• Udemy’s “Python for Data Science and Machine Learning”

Choose the Right Learning Path

If you’re serious about building a career in AI, consider:

• Picking a course or program that includes hands-on projects.
• Learning specific topics like machine learning, deep learning, or natural language processing.
• Selecting a school or platform that fits your budget and learning style.

Also, make sure they provide access to tools and labs where you can practice what you learn.

Get Certified

Certifications help you stand out in the job market. They show employers that you know the basics and are committed to learning.

Helpful AI Certifications:

• IBM AI Engineering on Coursera
• Google AI Certification
• Microsoft Azure AI Engineer Associate

Certifications can give you a real edge if you want to work with companies focused on self-driving cars, robots, or data analytics.

Build Real-World Experience

After learning the basics, try to:

• Work on small AI projects at home or through online competitions like Kaggle.
• Apply for internships or entry-level jobs in startups or research labs.
• Join open-source AI communities where you can learn and contribute.

This hands-on experience is key. Even small projects show your passion and skills to employers.

Apply for Jobs or Freelance Gigs

Once you’ve got some experience and knowledge, start applying for jobs. Begin with:

• Entry-level roles in AI or data science.
• Junior machine learning engineer positions.
• Freelance platforms offering AI-related gigs.

Start small, build your confidence, and grow from there.

Common Job Roles in the AI Field

Here are some popular job roles you can explore:

• Data Scientist: Analyzes large sets of data to find useful insights.
• Machine Learning Engineer: Creates programs that allow machines to learn from data.
• AI Developer: Builds smart software using AI techniques.
• Analytics Engineer: Helps businesses make data-driven decisions.
• DevOps Engineer (AI): Supports teams by building tools to manage AI models and systems.

Skills You Need to Become an AI Engineer

Let’s break the skills into two groups—technical and soft skills.

Technical Skills:

• Math: Understanding basic concepts like statistics and probability is important.
• Programming: Learn Python—it’s the most popular language in AI.
• Data Analysis: Know how to collect and understand data.
• Algorithms: Learn how AI models are built and work.
• AI Tools: Get hands-on with tools like TensorFlow, PyTorch, or Scikit-Learn.

Soft Skills:

• Communication: You need to explain complex ideas in simple terms.
• Teamwork: AI projects often require working with other developers and data scientists.
• Problem-solving: AI is all about finding smart solutions to hard problems.
• Leadership: As you grow, leading a team will become important.
• Curiosity: Always stay updated with new tech and research in AI.

Typical Salary of an AI Engineer

AI Engineers are in high demand—and they get paid well.

• In India, the average salary is around ₹9.75 lakhs per year.
• In the US, it’s about $120,000 per year, and even higher for experienced professionals.

With more experience (5–6 years), salaries can reach ₹17–20 lakhs per year in India and over $150,000 in the US.

How to Build a Long-Term Career in AI

Here are a few smart moves to grow your career:

• Keep learning. AI is a fast-moving field—don’t stop updating your skills.
• Create a strong portfolio with real-world projects.
• Join professional AI groups and forums to network.
• Attend workshops, hackathons, or tech meetups.
• Follow AI experts on platforms like LinkedIn and YouTube.

Parting Thoughts

AI Engineering could be your dream job if you’re curious about how things work, love solving problems, and enjoy learning new skills. And the best part? You don’t need to be a math genius or coding expert. With the right learning path and determination, you can quickly become an Artificial Intelligence Engineer, even from scratch.

Start small. Stay consistent. And don’t be afraid to fail and learn along the way. The world of AI is waiting for you! 

AI and data science go hand in hand; while AI builds smart systems, data science provides the fuel—data. To gain a solid foundation in these areas, consider joining Data Science courses by Pickl.AI, which offer practical training, mentorship, and certification to help you get job-ready fast. 

Start learning, keep building, and become the AI expert the future needs.

Frequently Asked Questions

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

Depending on your background and learning pace, it can take anywhere from 6 months to 2 years. With focused study, hands-on projects, and the right courses, you can fast-track your way into an AI career.

Do I need a degree to become an artificial intelligence engineer?

No, a traditional degree isn’t mandatory. Online certifications, bootcamps, and self-paced learning can also prepare you with the skills needed to enter the AI field and secure a job.

What programming languages should an artificial intelligence engineer learn?

Python is the most recommended language due to its rich AI libraries, such as TensorFlow and PyTorch. Other useful languages, depending on specific AI roles and projects, include R, Java, and C++.