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Home » A career in Artificial Intelligence – The 2 Most Electrifying Buzzwords Today

A career in Artificial Intelligence – The 2 Most Electrifying Buzzwords Today

Are you fascinated by the world of Artificial Intelligence and wondering about the exciting career opportunities it offers? You’re not alone! With AI being one of the fastest-growing fields, there has never been a better time to consider a career in artificial intelligence.

From chatbots to autonomous vehicles, AI is transforming the way we live and work. As more companies across various sectors look to harness the power of AI, the demand for skilled AI professionals is on the rise.

So, what kind of careers are available in AI? The answer is, plenty! AI is a vast field that encompasses various subfields such as machine learning, natural language processing, computer vision, robotics, and more. Some of the most sought-after AI roles include Data Scientist, Machine Learning Engineer, AI Researcher, AI Architect, and Robotics Engineer.

A career in AI can be both rewarding and challenging. With the demand for AI professionals only increasing, there has never been a better time to enter this exciting field. Whether you’re a recent graduate or an experienced professional, there are plenty of opportunities available for those willing to learn and stay up-to-date with the latest advancements in AI.

Fascinated? Want to know more? Come along…

What is Artificial Intelligence?

Artificial Intelligence (AI) let machines (mostly computer systems or other machines embedded with computer programs) do things or perform tasks that require human intelligence such as (a) recognition of speech and communicating, answering question, giving insights, reacting to human emotions, etc.; (b) search for information and convey the search results; analyze information to recommend possible decision options; (c) recognize images, view objects and scenes, etc. and make inferences from observation; (d) switch on / switch off as well as control operations of a machine, equipment, or device; (e) High tech Artificial Intelligence can assimilate knowledge from various sources as humans do, learn from the experience of conversations with humans, understand human emotional reactions, and then make decisions based on learning and experience.

What does an Artificial Intelligence Engineer do?

An Artificial Intelligence (AI) Engineer is a professional who specializes in building and maintaining complex AI systems. They are responsible for designing, developing, testing, and deploying AI models and algorithms that can perform various tasks such as natural language processing, speech recognition, computer vision, and more.

The AI Engineer’s role involves working closely with data scientists, machine learning engineers, software developers, and other cross-functional teams to build robust and scalable AI solutions. They also need to have a deep understanding of the latest AI technologies, programming languages, and frameworks to stay up-to-date with the rapidly evolving AI landscape.

Some of the key responsibilities of an AI Engineer include:

  1. Developing AI models and algorithms – AI Engineers design and develop complex algorithms and models that can analyze and interpret large volumes of data.
  2. Selecting and implementing AI tools and frameworks – They evaluate and select the best AI tools and frameworks that can help solve specific business problems and optimize system performance.
  3. Building and maintaining AI infrastructure – AI Engineers develop and maintain the infrastructure required to run AI applications, including cloud platforms, data storage systems, and computing resources.
  4. Testing and validating AI models – They test and validate AI models to ensure they are accurate, reliable, and efficient.
  5. Collaborating with cross-functional teams – AI Engineers work closely with data scientists, machine learning engineers, software developers, and other teams to develop end-to-end AI solutions.
  6. Monitoring and optimizing AI performance – They monitor the performance of AI systems and optimize them for improved accuracy, efficiency, and scalability.

Why become an Artificial Intelligence Engineer?

Pursuing a career in Artificial Intelligence (AI) Engineering is a highly rewarding and challenging career path that offers many benefits. Here are some of the reasons why you should consider pursuing a career as an AI Engineer:

  1. High demand: With the rise of AI, the demand for AI Engineers is increasing rapidly. According to the World Economic Forum, AI-related jobs are expected to create 2.3 million jobs by 2022. This high demand translates into attractive salaries and excellent job prospects.
  2. Innovative field: AI is an innovative and rapidly evolving field, and as an AI Engineer, you will be at the forefront of this exciting technological revolution. You will have the opportunity to work with cutting-edge technologies, solve complex problems, and push the boundaries of what is possible with AI.
  3. Variety of roles: AI is a vast field that encompasses many subfields such as machine learning, natural language processing, computer vision, and robotics. As an AI Engineer, you can choose to specialize in one or more of these areas and work in a variety of roles such as data scientist, machine learning engineer, AI researcher, and robotics engineer.
  4. Impactful work: AI has the potential to transform many industries, from healthcare and finance to manufacturing and transportation. As an AI Engineer, you can work on projects that have a real impact on people’s lives and society as a whole.
  5. Continuous learning: AI is a field that requires continuous learning and upskilling. As an AI Engineer, you will have many opportunities to learn new skills, work on challenging projects, and stay up-to-date with the latest AI technologies and advancements.

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Following are the popular specialization in artificial intelligence careers-

Machine Learning Engineer / Expert

AI is the technology and ML is one of the many techniques to bring about this technology. Machine Learning is a current application of AI based on the idea that machines when given access to data, can learn for themselves. ML Experts can make computers capable of reading text and deciding whether the person who wrote the text is complaining or congratulating; then listening to a piece of music, understanding whether it is likely to make someone happy or sad, and finding other pieces of music to match the mood; even composing their music expressing the same themes which they know is likely to be appreciated! ML experts can help machines understand the vast nuances of human languages too, and to learn how to respond in a way we can comprehend.

AI Expert (Natural Language Processing – NLP and Speech Recognition)

This is a subset of AI. NLP and Speech Processing experts write codes to enable computers to communicate with people using everyday language. They deal with the conversion of information from the computer database into readable human language and vice versa. Speech Recognition involves phonetics and word recognition (the way humans of different nationalities speak a language or the same language with distinct dialects).

AI Expert (Deep Learning)

Deep learning is powerful because it makes hard things easy for machines! DL Experts work towards making a machine/computing device excel at identifying patterns in unstructured data. For example, a cluster of 16000 computers has been successfully trained by DL Experts to recognize a cat based on 10 million digital images (unstructured data) taken from YouTube videos! This may be an easy task for a human brain but such a task of ‘learning from experience’ is too tough for a computer!

AI Expert (Cognitive Computing)

How humans to approach problem-solving is the primary focus of this sub-field of AI. Siri, Google Assistant, Cortana, and Alexa are a few of the best illustrations of exemplary contributions of AI Experts specializing in Cognitive Computing, all with a common goal of simulating human thought processes in a computerized model. Similarly, IBM’s cognitive computer system, Watson (development team led by principal investigator David Ferrucci) could help in lung cancer treatment in NY. Today around 80% of nurses working with Watson follow its guidance.

AI Expert (Avionics AI)

The ultimate motives of AI Experts practicing in the field of Avionics are to bridge the gap between the pilot driver and the control systems in an operating air vehicle or enhance functionalities of the controlling systems in an unmanned aerial vehicle (UAV) such as satellite systems or space rotors etc. which are not physically driven by humans. AI Avionics Experts design intelligent systems which can process information from multiple sensors & sources and present it to the pilot enabling her to make an informed decision while on a flight or take critical decisions in the absence of a human driver. Futuristically, this wave can potentially even remove the need for a pilot in the future.

AI Expert (Predictive APIs)

This is also a sub-branch of AI. Prediction is to guess the future before it happens. Prediction in technical terms is analyzing a set of data and finding out whether something is going to happen or not and this is what AI Prediction Interface Engineers do. For example, weather widgets on your phone specially written by AI Prediction Application Expert teams can predict future temperatures from a set of data of the last 10 or more years. Similarly, AI Application Program Interface Experts specializing in the field of Prediction Interfacing have composed the Google Prediction API to try to guess what your next words in an email can be, or whether you want to attach a file to your mail when you have written the word attachment but have forgotten to finally attach it.

AI Expert (Visual Computing and Image Recognition / Computer Vision)

AI Experts practicing in this sub-domain deal with how computers can be made to gain a high-level understanding of digital images or videos. In most computer applications earlier, they were pre-programmed to solve a particular task, but AI Experts are now building methods based on learning which are today becoming increasingly common. This field can be considered as a sub-branch of ‘Deep Learning’ too.

Some AI Researchers are today focusing on healthcare by enabling a computer to assess a few photographic data to diagnose a patient such as detecting the presence of tumors or measuring organ dimensions etc. Some more AI Expert Teams are concentrating on application areas in manufacturing: quality control via “seeing” details of final products for automatic inspecting to find defects. In defense, a major field of application will be missile guidance or framing the path to be followed by a fired missile.

AI Expert (Robotics, Gesture, and Motion Control)

This is a vast field that reigns in AI Experts along with Mechanical and Electronics Engineers who together train robotic devices to display motion in a specific manner or execute tasks that require movements in different directions or produce an action that is governed by specific inputs from a human user. Some AI Experts today are focusing on writing codes to enable intelligent robots to mimic a human by capturing image data such as following the dance steps of a human and imitating them etc. This branch will also include AI Experts and Mechatronics Engineers collaborating to build medical prosthetics like an electronic arm/leg that can follow instructions from the brain without any text or voice input from the human user. These are also examples of ‘smart’ wearable devices.

Steps to become an artificial intelligence

Pursuing a career in artificial intelligence (AI) can be a rewarding and challenging experience. Here are some steps you can take to pursue a career in AI:

  1. Develop a strong foundation in mathematics and computer science: Mathematics and computer science are the foundation of AI. You should have a solid understanding of linear algebra, calculus, probability theory, statistics, and algorithms.
  2. Learn programming languages: You need to be proficient in programming languages like Python, R, C++, and Java, as they are commonly used in AI projects.
  3. Get a formal education: Consider getting a degree in computer science, data science, or AI. Many universities offer undergraduate and graduate programs in these fields.
  4. Take online courses: You can take online courses to supplement your education, learn about the latest AI technologies and tools, and gain practical experience.
  5. Participate in AI projects: Participate in open-source AI projects, competitions, or internships to gain practical experience in AI.
  6. Build your AI projects: Building your own AI projects can demonstrate your skills and provide a portfolio to showcase your work.
  7. Network with AI professionals: Attend AI conferences, join online forums and communities, and connect with AI professionals to learn about job opportunities and stay up-to-date with the latest developments in the field.
  8. Keep learning: AI is a rapidly evolving field, and you need to keep learning and updating your skills to stay relevant. Continuously educate yourself through reading research papers, taking courses, and working on AI projects.

Eligibility criteria to pursue a career in artificial intelligence

To be eligible to become an artificial intelligence engineer, they need to have a foundational education that meets the requirements of the field of artificial intelligence.

The first step to becoming an artificial intelligence engineer would be to get a bachelor’s degree. You can pursue an undergraduate or graduate degree in any of the following streams.

  1. Information technology
  2. Informatics
  3. Data Science
  4. Statistics
  5. Economy
  6. Mathematics
  7. Finances

If you wish, a master’s or postgraduate degree in the above courses will also be beneficial.

Apart from this, there are several different certification courses like machine learning and data science that many online class programs offer that you can take to improve your knowledge.

Skills required to pursue a career in artificial intelligence

To pursue a career in artificial intelligence (AI), you need to have a combination of technical and soft skills. Here are some of the key skills required to pursue a career in AI:

Strong foundation in mathematics and computer science

You should have a good understanding of linear algebra, calculus, probability theory, statistics, and algorithms. You should also be proficient in programming languages such as Python, R, C++, and Java.

Data analysis and data management skills

You should be able to analyze large amounts of data, identify patterns, and extract meaningful insights. You should also be familiar with databases and data management systems.

Machine learning and deep learning

You should know about machine learning and deep learning algorithms, and be able to apply them to solve real-world problems.

Natural language processing (NLP)

NLP is an important component of AI, and you should have a good understanding of NLP techniques, such as sentiment analysis, named entity recognition, and text classification.

Problem-solving and critical thinking

You should be able to identify problems, break them down into smaller components, and develop solutions to solve them. You should also have strong analytical and critical thinking skills.

Creativity and innovation

AI requires creative and innovative thinking to develop new algorithms and solutions.

Communication and teamwork

You should be able to communicate your ideas effectively and work collaboratively with team members and stakeholders.

Continuous learning and adaptability

AI is a rapidly evolving field, and you should be able to keep up with the latest developments and adapt to new technologies and techniques.

Fees required to pursue a career in artificial intelligence

The fees required to pursue a career in Artificial Intelligence (AI) in India can vary widely depending on several factors such as the type of program, the institute or university offering the program, the duration of the program, and the level of the program (undergraduate or graduate). Here’s a general overview of the fees you can expect to pay for various AI programs in India:

  1. Bachelor’s degree: The fees for a four-year undergraduate degree program in AI or related fields can range from INR 3 lakh to INR 10 lakh.
  2. Master’s degree: The fees for a two-year postgraduate degree program in AI or related fields can range from INR 2 lakh to INR 15 lakh.
  3. Diploma and certification courses: The fees for diploma and certification courses in AI or related fields can range from INR 50,000 to INR 2 lakh.
  4. Online courses: Online courses in AI are becoming increasingly popular and can range from INR 10,000 to INR 1 lakh, depending on the duration and the type of course.

Some of the top universities in the world working in the area of AI are:

  • IIT Delhi
  • IIT Bombay
  • IIT Kharagpur
  • IIT Kanpur
  • IIT Madras
  • IIT Patna
  • IIT Hyderabad
  • IIIT Hyderabad
  • IIT Jodhpur
  • Johns Hopkins
  • MIT
  • Nanyang Technological University

Job opportunities after studying Artificial Intelligence

If you want to get a good opportunity in Artificial Intelligence after your Bachelor’s degree, then you need to pass out with an Engineering degree from a premier Engineering institution. Or else, you can work in software engineering and development for a few years (say, about 4-5 years). You may then attempt to find an internship also before you look out for a full-time opportunity.

After your Master’s degree in a suitable field, you can get a good start. But in this case, also, doing an internship first will help you to land a good job in AI.

If you plan to do a Doctoral degree first, it could be a really good idea as a Ph.D. can open up great research opportunities for you.

In most cases, at the beginning of your career, depending upon your educational qualifications as well as skills, you will start in any one of the following or similar positions:

  • AI Engineer
  • AI/ML Engineer
  • Computational Neuroscientist
  • Computational Scientist
  • Data Scientist
  • Data Scientist (AI)
  • Engineer (Machine Learning)
  • Machine Learning Engineer
  • Research & Development Engineer
  • Research Engineer
  • Research Scientist
  • Software Development Engineer – AI
  • Software Engineer (AI/ML)

You may also get opportunities in research and teaching at some of the top universities in the world. But to get an opportunity there, you must complete your MS and Ph.D. from one of the top Universities in the world teaching Computer Science and related subjects.

In a university, after your Ph.D., you may get an opportunity to a:

  • Research Associate
  • Post-Doctoral Fellow
  • Assistant Professor/ Similar position

Various types of companies may recruit you:

  • Internet and IT giants such as IBM, Google, Microsoft, Facebook, Amazon Services Inc., Tencent, Twitter, etc.
  • Other IT companies focused on software engineering in the field of AI such as IPSoft, OpenAI, AlphaSense, AIBrain, CloudMinds, Deepmind, H20, Iris AI,, etc.
  • Companies that design and develop various microprocessors/ electronic systems/devices/applications, advanced semiconductor technologies for industrial clients or bulk consumption such as Apple Inc., Intel, Nvidia, Qualcomm, Cisco, Samsung Electronics, Siemens, Intel, Verizon, Ericsson, Oracle, SAP, IMEC, Nokia, Symantec, etc.
  • Automotive and transportation systems manufacturers (some of these are suppliers of automotive technology for the biggest car manufacturers in the world) include aerial flight systems such as Toyota, Tesla, Volvo, Autonodyne, Xevo, Nuance Automotive, Hyundai, etc.
  • Space research and administration organizations such as NASA, ISRO, etc.
  • FinTechCompanies that are into the BFSI industry such as insurers, consultancies, financial institutions, investment banking companies, or others like Kasisto, Tesorio, Splunk, YotaScale Inc, Zestfinance, Scienaptic Systems, Underwrite.Ai, Kensho, etc.
  • Health Tech companies such as MetaMind are involved in deep learning networks, image recognition, text analysis, and machines/systems/devices to cater to the healthcare sector.
  • Technology/research divisions of Deloitte, Goldman Sachs, and JP Morgan Chase.

Artificial Intelligence Engineer salary in India

After your Bachelor’s degree, you may expect to make about Rs. 50,000 – 1,20,000 or even more a month. Higher salaries are paid to graduates from premier engineering institutions.

At the entry-level jobs, after your Master’s degree, depending upon the institution where you are graduating from and your skills, you may expect to get about Rs. 70,000 – 1,50,000 or even more a month. In junior-level jobs (after 4-5 years of experience), you can make about Rs. 80,000 – 2,50,000 or more per month.

In mid-level jobs in India (after having 8-12 years of experience), you can expect to earn about Rs. 1,50,000 – 4,00,000 or even more a month.

In senior-level jobs in India (after having 15 or more years of experience), you can expect to earn about Rs. 2,50,000 – 8,00,000 or even more a month.

Career progression in Artificial Intelligence

At the beginning of your career after your Master’s or Ph.D. (in some cases, after your Bachelor’s degree), you will be placed in positions such as AI Engineer, AI/ML Engineer, Data Scientist, Data Scientist (AI), Engineer (Machine Learning), Research & Development Engineer, Research Engineer, Research Scientist, Software Development Engineer – AI, Software Engineer (AI/ML) or in similar positions.

You will progress as:

Senior Engineer/ Senior Scientist/ Senior R&D Engineer/ Senior Research Scientist – Lead Engineer / Principal Scientist – Principal Engineer / Principal Scientist – Director – Vice President – President / Chief Engineering Officer / Chief Scientific Officer

In a university, after your Ph.D., you may get an opportunity as a Research Associate, Post-Doctoral Fellow, or an Assistant Professor/ Similar position.

If you are in a teaching position, you will progress as:

Assistant Professor – Associate Professor – Professor – Dean/ Director or in similar positions.


The future of this pathway looks bright and this industry is poised to grow strong in the upcoming years. Some of the prominent key growth factors that the market seems witnessing include an increasing number of end users, growing research & development activities, raising the usage of smart wearables/devices, and widespread industrial automation.

The Global AI market accounted for $15.70 billion in 2017 and is expected to reach $300.26 billion by 2026 growing at a rate of 38.8% compounded annually during 2017-2026.

The global demand for AI & Robotics systems is anticipated to be driven by the massive investments made by countries such as China, the US, Russia, and Israel in the development of next-generation systems and the large-scale procurement of such products by countries like Saudi Arabia, Japan, India, and South Korea.

Isn’t it amazing? We hope we have served you with enough information! If you still got questions, reach out to us!

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Also Read:

Career in Artificial Intelligence: FAQs

What jobs can I get with a degree in artificial intelligence?

With a degree in artificial intelligence, you can pursue various job roles, including machine learning engineer, data scientist, AI researcher, AI product manager, AI consultant, and more.

Do I need a master’s degree to work in AI?

While a master’s degree in artificial intelligence or a related field can give you an advantage, it is not always necessary. Many AI professionals have a bachelor’s degree in computer science, data science, mathematics, or a related field, along with practical experience in AI.

What programming languages should I learn for AI?

The most commonly used programming languages for AI are Python, R, C++, and Java. Python is the most popular language for machine learning and data analysis.

What are the benefits of a career in AI?

A career in AI offers various benefits, including high demand and job security, attractive salaries, the opportunity to work on cutting-edge technology, and the ability to make a positive impact on society.

What industries can I work in with AI skills?

AI skills are in high demand in various industries, including healthcare, finance, e-commerce, gaming, transportation, manufacturing, and more. You can work in any industry that requires data analysis, prediction, automation, or optimization.

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