As The CEO of Amazon, Jeff Bezos, said, “AI has the potential to revolutionize entire industries and improve our daily lives in countless ways.”
Along with the demand of artificial intelligence (AI) continues to grow, so too does the need for skilled AI engineering who can design and build advanced machine learning systems.
But what does it take to become an AI engineer, and how can you break into this exciting and rapidly-evolving field?
As an AI engineer, you can design and build advanced machine learning systems for tasks such as speech recognition and image processing.
If you have a background in software programming and want to pursue a career in AI, becoming an AI engineer may be the right path for you.
The Rapid Growth of AI Tech
Artificial intelligence (AI Tech) has seen rapid growth in recent years, leading to debate about its potential impact on employment.
While some believe that AI could lead to job displacement, others argue that it can make tasks easier and complement human work.
Despite differing views, the AI industry continues to thrive and there is a high demand for skilled AI professionals, especially AI engineers.
According to a report by the Organization of Economic Co-operation and Development (OECD), the advancement of AI has actually increased the need for computer-skilled jobs. In this article, we will explore the steps to becoming an AI engineer, a sought-after career in the growing field of AI.
Understanding Artificial Intelligence
Artificial intelligence (AI) is the ability of a computer or machine to perform tasks that normally require human intelligence, such as learning, problem-solving, and decision-making.
There are several types of AI, including rule-based AI, machine learning, neural networks, and natural language processing.
AI has the potential to transform many industries and has raised ethical concerns about its potential risks and benefits.
Compelling Reasons Why Becoming AI Engineer is Quite Promising!
Around 4.5 million engineers graduated in the US in 2020. But if you’re wondering whether there are enough jobs to go around, there’s no need to worry.
There are several compelling reasons to consider becoming an AI engineer!
1. High Demand for AI Skills
AI and machine learning are some of the most in-demand skills in the tech industry.
According to a report from Indeed, the number of job postings for AI-related roles grew by 119% between 2015 and 2020.
As a result, AI engineers with the right skills and experience can command high salaries and have a wide range of job opportunities.
2. Exciting and Fast-Growing Field
AI is a rapidly growing field with many exciting developments and breakthroughs happening all the time. As an AI engineer, you would have the opportunity to work on cutting-edge technologies and be at the forefront of innovation.
3. Positive Impact on Society
AI has the potential to improve and transform a wide range of industries and sectors, from healthcare and education to finance and transportation. As an AI engineer, you would have the opportunity to contribute to this positive impact and make a difference in the world.
4. Personal and Professional Growth
Working as an AI engineer can be challenging and rewarding, and it provides opportunities for personal and professional growth.
You will have the chance to develop your skills and knowledge in a dynamic field and work on projects that have the potential to make a real impact.
What Does The AI Engineers Do?
As an AI engineer, you will use machine learning algorithms and deep learning neural networks to build AI models that can draw business insights and inform decision-making within an organization. You may work on creating weak or strong AI systems, depending on the goals of the project.
To be successful in this role, you should have a strong foundation in programming, software engineering, and data science. You will use various tools and techniques to process data and develop and maintain AI systems.
AI engineers work on a range of AI applications, such as sentiment analysis-based contextual advertising, visual identification and perception, and language translation. In the next section, we will explore the responsibilities of an AI engineer in more detail.
Responsibilities on AI Engineering:
- Converting machine learning models into application program interfaces (APIs) so that other applications can use them
- Building AI models from scratch and helping different teams within the organization (such as product managers and stakeholders) understand the results they get from the model
- Setting up data ingestion and transformation infrastructure
- Automating the infrastructure used by the data science team
- Performing statistical analysis and tuning the results to help the organization make better-informed decisions
- Managing AI development and product infrastructure
- Being a team player, as coordinating with others is essential.
It’s important to get a high school diploma with a focus on scientific subjects like chemistry, physics, and mathematics. This will give you a strong foundation for your subsequent learning.
Next, you’ll want to complete a bachelor’s degree in a related field like computer science or information technology.
Some universities offer specialized AI engineering programs, while others allow you to focus on AI as part of a broader degree. To get into top engineering schools like NITs, IITs, and IIITs, you may need to do well on the Joint Entrance Examination (JEE).
If you want to give yourself a competitive edge and increase your earning potential, you may consider pursuing a master’s degree in a related field like AI or machine learning.
This can provide you with a more comprehensive understanding of the subject and specialized technical skills. You may need to take the GATE exam to enroll in an engineering program.
In addition to formal education, you can also consider earning certifications through online courses or diploma programs.
These can help you increase your skills without the financial investment of a full degree program.
- Programming languages: AI engineers should be proficient in at least one programming language, such as Python, Java, or C++.
- Machine learning: AI engineers should have a deep understanding of machine learning algorithms and how to implement them. This includes topics like supervised and unsupervised learning, regression, classification, and clustering.
- Deep learning: Knowledge of deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), is important for many AI projects.
- Data engineering: AI engineers should be skilled in extracting, cleaning, and preparing data for use in machine learning models. This includes knowledge of SQL, data pipelines, and data storage technologies.
- Software engineering: AI engineers should have a strong understanding of software development principles and be able to design, test, and maintain AI systems.
- Statistics and mathematics: A background in statistics and mathematics is essential for understanding and implementing machine learning algorithms.
AI Engineering Salary
According to Glassdoor, the average annual salary of an AI engineer is $114,121 in the United States and ₹765,353 in India. The salary may differ in several organizations, and with the knowledge and expertise you bring to the table.