How to Become an AI Engineer: Skills, Roles and Career Guide

Introduction: Why AI Engineering Is One of the Hottest Careers Today

All of us are aware how cautiously AI (Artificial Intelligence) has become a significant part of our lives and also increasingly popular today. This is one such industry that has been in demand for quite some time now, owing to its ability to produce scalable solutions, smart and encrypted applications, alongside allowing us to embrace consistent industry developments, and ensuring seamless user experience.

Some of the crucial aspects that make AI engineering a promising career for this generation include quite a few things. We have elaborated on how a career in artificial intelligence looks like:

1. Growing demand amongst the youth and industry professionals
2. Competitive salary bracket and salary growth potential
3. Scope of developing high-end technology and investing in interesting, innovative work
4. Chance of exploring multiple career options, ensuring extensive relevance in the industry
5. Requirement for special skillset which prepares an individual to stay in demand and marketable

What Is AI Engineering?

AI engineering encompasses curation of tools, systems, and processes which utilize the scope of artificial intelligence in the best possible manner. It is regarded as a technical branch of knowledge that focuses on the design, development and deployment of AI systems. Some popular examples of AI engineering that we witness everyday include the popular social media channels, like instagram, youtube, etc. and information mediums like twitter and facebook, entertainment and live streaming platforms like netflix and amazon prime that show us recommendations as per our preference and behaviour.

In simple terms, we can define AI engineering as the methodology or practice to transform AI data and research into real-world applications.

What Does an AI Engineer Do?

Before we discuss what an AI engineer does, let us take you through who AI engineers are. An AI engineer is the one who is responsible for the development and deployment of AI systems and protocols and accountable for combining software with data science. Every AI engineer remains an inseparable part of the organisation that essentially relies on artificial intelligence where they develop, strategize and train complex algorithms to act as a human. Their core responsibilities include:

1. Developing effective AI strategy
2. Identify all key problems that AI can resolve
3. Building and administering AI development from scratch and production systems
4. Carry out in-depth analysis for the production outcome
5. Contribute to the policy making process of the company
6. Assist and guide the product managers and stakeholders with the AI model implementation
7. Ensure effective collaboration across teams to ensure AI advocacy

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Career Paths in AI Engineering

The professional roadmap of an AI engineer somewhat flows like the picture shown below. Primarily the career chart of an AI engineer involves the core areas like designing , development, deployment, data management, etc. and the roles essentially involved in this profession include:

1. ML engineers
2. Data scientists
3. AI research scientists
4. AI product managers
5. Computer vision engineers
6. ML operations engineers

And being a preferable career choice, all these profiles can fit across all the popular industries of the economy including finance, transportation, healthcare, manufacturing and retail.

Step-by-Step Guide: How to Become an AI Engineer

It is not a difficult job at all for anybody who aims to become an AI engineer. All it requires of an individual is to have primary computer knowledge, a certain amount of comprehension of information and technology (IT), mathematical efficiency, proficiency in Python and understanding of the major neural networks, frameworks and cloud platforms. 

A few other technical skills an individual can have to be a part of this profession include programming, data engineering, API development, etc. and a set of soft skills including problem solving, effective communication, etc.

We have elaborated below as to how an individual’s deep-rooted knowledge in each of these areas is going to be beneficial.

• Build a Strong Programming Foundation - All the inspiring AI engineers are supposed to establish a solid understanding of programming. The foundation of programming involves mastering Python and having a clear understanding of software engineering, development and knowledge of data and algorithms.
   
• Master Mathematics and Statistics for AI - If AI engineers prioritize knowing more about the core arithmetic disciplines like linear algebra, calculus and probability, they can perform incredibly in their respective roles. There are several courses and training programs that individuals can take up to know more about optimization, probability, implementation, etc.
   
• Learn Machine Learning and Deep Learning - AI engineers must hold a significant understanding of machine learning and deep learning. This can be achieved with elaborate training of Python, statistics and linear algebra and simultaneously mastering frameworks like PyTorch and TensorFlow. 
   
• Get Hands-On With AI Tools and Frameworks - This again can be achieved with a strong foundational learning of Python and mathematics that allows AI engineers to gain first-hand experience through project handling tasks. Additionally, learning PyTorch, TensorFlow, Scikit-learn, Natural Language Processing and Large Language Modules will also help.
   
• Earn AI Certifications and Take Courses - There are several courses and training that AI engineers can pursue to earn certifications and establish their credibility in the industry. These courses are easy to pursue with a streamlined enrollment process. Some of the popular courses that AI engineers can take up are - Google Cloud Machine Learning Engineering, Microsoft Certified: Azure AI Engineer Associate, IBM AI Engineering Professional Certificate, Artificial Intelligence Graduate Certificate by Stanford University School of Engineering, MIT's Professional Certificate Program in Machine Learning and Artificial Intelligence, Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning, etc.
   
• Build Projects and Portfolio - AI engineers must prioritize a professional portfolio that includes project development, develops applications that can be deployed easily and proper visual presentation of the same on GitHub. This includes proper documentation, integration of AI with database and user interface. 
   
• Apply for Jobs, Internships, and Entry-Level Roles - Just like how professionals across all industries seek jobs, pursue internships and take up entry-level positions to kickstart their career, all the aspiring and professional AI engineers can go with the same approach to find their grounding - a career in artificial intelligence. To ensure things escalate well, it is crucial for them to have an appealing portfolio.

Essential Artificial Intelligence Skills Required to Become an AI Engineer

Becoming an engineer, especially an AI engineer is all about having the right blend of skills, knowledge and practice, functionally put together to decipher complex problems and develop workable solutions for those problems. Primarily, there are three major AI engineer skills that one must possess. They are:

1. Technical Skills - Some of the essential skills that are considered valuable in an AI engineer’s portfolio include - their knowledge of Python, Advanced Programming, Machine Learning, Mathematics and Statistics, along with their understanding of Deep Learning Frameworks, Data Analysis (Handling and Processing), etc.
    
2. Soft Skills for AI Engineers - Strong communication, team-management skills, leadership ability, analytical skills, adaptability, ethical awareness, etc. are the core soft skills that the AI engineers should work towards, in order to build themselves appropriately for the particular role.
    
3. Advanced Skills for Career Growth - AI engineers can work towards developing their core engineering knowledge, in addition to holding data science and machine learning skills with practical use. As this field is currently one of the fastest growing professions, employees with moderate to high level experience are required to constantly upskill to stay relevant.

AI Engineer Salary and Career Growth

AI engineers are one of the highly paid professionals at present. Owing to their complex work system, they are increasingly in demand by companies and businesses. As there is not one role that is a part of this profile, multiple AI engineer designations earn a specific salary. Their salary chart and career growth can easily be interpreted from the following chart.

Future Scope of AI Engineering

AI engineering is a potentially growing field that offers immense scope. Today, nearly every industry is gradually inclining towards the adoption and application of Artificial Intelligence and Machine Learning. This incredible demand for AI makes it a highly desirable career option for the employed individuals. Not just that, AI Engineering also promises:

1. Remarkable industry transformation with AI implementation
2. Incredible demand from diversified industries, which forecasts high job growth
3. Workflow automation, effective decision- making, user-friendly experiences and business growth
4. A secure future by embedding skills that promise long-term relevance and ensured growth
5. Extensive career stability and incessant job opportunities
6. Constant innovation, development and therefore consistent learning

AI Engineer vs Data Scientist vs ML Engineer

Most of us remain puzzled with these terms which almost sound and feel similar. But all the three roles are different. While AI engineers, as elaborated above, are primarily responsible for building applications, a data scientist essentially studies and analyzes insights, and machine learning engineers release the final product or system. To make sure you gain a deeper understanding of all the three, we have highlighted the core differences that make each one vary from the other.
 

Is AI Engineering Right for You?

People consider AI engineering to be an incredible job role that promises a fantastic career and topping salary. In fact, professionals across all industries consider this field to be a stepping stone in their professional graph. The core aspects that make AI engineering a prominent career are its focus on constant learning, innovation and growth. For individuals who are technically sound, have interest in knowing more about technology, then AI engineering is indeed the go-to option for you.

Who should learn AI?

Artificial intelligence is one such industry discipline that anybody can pursue irrespective of their educational background and expertise. One does not necessarily have to be technically strong to be able to take this up as their career option. So, if you are someone who loves learning, is inquisitive about technology and seeks interest in deciphering complex areas and is driven by productivity and growth, then this is the profession for you. Children, college students, professionals, industry leaders, business managers - everybody alike, can pursue AI without being apprehensive of their previous background.

Background requirements

To pursue AI engineering or to work as an AI engineer, one has to have a strong understanding and knowledge of computer science, mathematics, data research and evaluation. This respective job role becomes more significant if the individual additionally possesses machine learning and deep learning skills.

Most individuals who look forward to pursuing AI engineering, primarily are the ones holding specialization in computer systems, computer science or software. Additionally, holding professional certifications related to the field of AI, engineering or computer science or domains that call for advanced degrees also helps. For people who neither hold a bachelors or masters degree in the relevant subjects, can also take up courses or training from renowned educational institutions, easily available online. Apart from this, building ones’ technical skills and behavioral skills also adds a lot of value to an individual’s credibility.

Challenges to expect

Some of the primary challenges faced by AI engineers during their professional tenure are associated with:

1. Acquiring, preparing, maintaining and managing significant datasets as the final output heavily depends on the quality and the authenticity of the data.
2. Additionally, AI engineering (involving models) requires investment and regular maintenance.
3. Transforming old systems into new ones with proper integration of AI tools and techniques is an extensive process and also quite challenging.
4. Risks which arise from data regulation, its privacy and safety.
5. AI being a rapidly growing domain, makes it challenging for people to level up with it, which results in vacant positions due to unqualified candidates.
6. Changes in the workplace conditions or the real world, which often makes AI incompatible.

There are many more drawbacks associated with AI engineering and the entire domain, including ethical exploitation, secure usage of data, regulatory complexity, compromised transparency and many more.

Frequently Asked Questions

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

   It typically takes about one year of focused study, especially for freshers. Those who dedicate more time may finish sooner, while working professionals may need longer depending on their schedule.

2. Can a non-IT student become an AI engineer?

   Yes. Anyone can become an AI engineer by learning programming (Python), SQL, data structures, mathematics such as linear algebra and calculus, and machine learning concepts.

3. Is coding mandatory for AI engineering?

   Yes, coding is essential. Knowledge of Python, SQL, and technical fundamentals helps build AI models, tools, and algorithms effectively.

4. Which language is best for AI?

   Python is widely considered the best language for AI due to its simple syntax, extensive libraries, and strong community support. Other languages include C++, Julia, and Java.

5. Can I learn AI without a degree?

   Yes. A specialised degree is not mandatory. Developing AI skills, practical experience, and certifications can help you enter the field.

6. Is AI engineering future-proof?

   Yes, AI engineering is considered a promising career as the industry continues to grow with evolving technologies, tools, and innovations.

Final Takeaways

AI engineering is not confined to being only an industry discipline. Today it has significantly taken over all the small and big industries as every role is somewhere influenced or driven by artificial intelligence. AI engineering is now a major field of work, involving application development, refining work models and streamlining needs.