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TechieGen Career Guide

Artificial Intelligence Career Guide

TechieGen's Artificial Intelligence career guide is intended to help you take the first steps toward a lucrative career in AI. The guide provides an in-depth overview of the data skills you should learn, the best data training options, career paths in AI, how to become an AI Engineer, and more.

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15 Minute Read

What Is Artificial Intelligence?

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

In the simplest terms, AI which stands for artificial intelligence refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. AI manifests in a number of forms. A few examples are:

  • Chatbots use AI to understand customer problems faster and provide more efficient answers

  • Intelligent assistants use AI to parse critical information from large free-text datasets to improve scheduling

  • Recommendation engines can provide automated recommendations for TV shows based on users’ viewing habits

AI is much more about the process and the capability for superpowered thinking and data analysis than it is about any particular format or function. Although AI brings up images of high-functioning, human-like robots taking over the world, AI isn’t intended to replace humans. It’s intended to significantly enhance human capabilities and contributions. That makes it a very valuable business asset.

Artificial intelligence is a constellation of many different technologies working together to enable machines to sense, comprehend, act, and learn with human-like levels of intelligence. Maybe that’s why it seems as though everyone’s definition of artificial intelligence is different: AI isn’t just one thing.

Technologies like machine learning and natural language processing are all part of the AI landscape. Each one is evolving along its own path and, when applied in combination with data, analytics and automation, can help businesses achieve their goals, be it improving customer service or optimizing the supply chain.

Narrow (or “weak”) AI
Some go even further to define artificial intelligence as “narrow” and “general” AI. Most of what we experience in our day-to-day lives is narrow AI, which performs a single task or a set of closely related tasks. Examples include:

  • Weather apps

  • Digital assistants

  • Software that analyzes data to optimize a given business function

These systems are powerful, but the playing field is narrow: They tend to be focused on driving efficiencies. But, with the right application, narrow AI has immense transformational power—and it continues to influence how we work and live on a global scale.

General (or “strong”) AI
General AI is more like what you see in sci-fi films, where sentient machines emulate human intelligence, thinking strategically, abstractly and creatively, with the ability to handle a range of complex tasks. While machines can perform some tasks better than humans (e.g. data processing), this fully realized vision of general AI does not yet exist outside the silver screen. That’s why human-machine collaboration is crucial—in today’s world, artificial intelligence remains an extension of human capabilities, not a replacement.

Artificial intelligence has long been a subject of anticipation among both popular and scientific culture, with the potential to transform businesses as well as the relationship between people and technology at large. So, why is AI usage reaching critical mass today?

Because of the proliferation of data and the maturity of other innovations in cloud processing and computing power, AI adoption is growing faster than ever. Companies now have access to an unprecedented amount of data, including dark data they didn’t even realize they had until now. These treasure troves are a boon to the growth of AI.

A critical source of business value—when done right


AI has long been regarded as a potential source of business innovation. With the enablers now in place, organizations are starting to see how AI can multiply value for them. Automation cuts costs and brings new levels of consistency, speed and scalability to business processes; in fact, some Accenture clients are seeing time savings of 70 percent. Even more compelling, however, is the ability of AI to drive growth. Companies that scale successfully see 3X the return on their AI investments compared to those who are stuck in the pilot stage. No wonder 84 percent of C-suite executives believe they must leverage AI to achieve their growth objectives.


Artificial intelligence is not just about efficiency and streamlining laborious tasks. Thanks to machine learning and deep learning, AI applications can learn from data and results in near real time, analyzing new information from many sources and adapting accordingly, with a level of accuracy that’s invaluable to business. (product recommendations are a prime example.) This ability to self learn and self optimize means AI continually compounds the business benefits it generates.

In this way, AI helps businesses adapt at speed, with a regular stream of insights to drive innovation and competitive advantage in a world of constant disruption. When scaled, AI can become a key enabler of your strategic priorities—and even a lynchpin to survival: Three out of four C-suite executives believe that if they don’t scale artificial intelligence in the next five years, they risk going out of business entirely. Clearly, the stakes are high to scale AI. 3 out of 4 C-suite executives believe that if they don’t scale artificial intelligence in the next five years, they risk going out of business entirely.

 
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What Exactly Does An AI Engineer Do?

I would suggest everyone before following an artificial intelligence career path or any other career path. You should have explicit knowledge of what types of work and responsibilities would come to your hand. And this is the essential thing because it tells whether you are fit for that career option or not.

Also, it gives you positive inner vibes if you are knowing what exactly I am gonna do after spending so much time to learn all that required skill set.

Ok, so let’s understand what you are going to do as an Artificial Intelligence engineer.

  • Perform research to advance the science and technology of intelligent machines.

  • Implement and evaluate algorithms.

  • Data mining and analysis of the data.

  • Design, develop and maintain artificial intelligence-enabled managed services.

  • Perform research to make AI/ machine learning more applicable to real-world problems.

  • Architect, implement and test data processing pipelines.

  • Provide software design and programming support to the team.

 
 

How To Become An Artificial Intelligence Engineer [Step-By-Step] Guide

 

Many people want to learn Artificial Intelligence but they don’t. One of the biggest reason is they actually don’t know where to start learning this overwhelming technology.

So the confusion on How to get started actually stopping many people to make their first move on learning Artificial Intelligence.

But don’t worry this Step-by-Step guide help you to make your first move towards an Artificial Intelligence career path. And you will be known, what are the things I should start learning first.

[Step 1] Mathematics

Strong your mathematics and sharpen your mind by solving logical problems. This will help you to understand and create logics in the programmes, and eventually help you to learn programming language fast, which is the step 2. In order to develop better applications and programmes you have to think more precisely and accurately (especially if you are writing codes for AI), which is only achieved if you are good at mathematics.

[Step 2] Programming Language    

As I have already told, Python is one of the best programming languages for Machine Learning/AI. So why go for other programming languages. You can learn other languages though, it’s always good to learn new and more things but after the Python. Here the key point is if you want to enter into this very domain you have to learn Python. However this not completely true. So why I am saying that because Python has more machine learning libraries and developer community support as compared to other programming languages, most of the industries are working with Python.

Thus if you go for Python then you will make easier for yourself to get into Artificial Intelligence career path. The choice is yours!

How to Learn?

[1] Follow up this website to learn the basics of Python https://www.learnpython.org

[2] Watch Python video tutorials on YouTube

[Step 3] Algorithms

The algorithm plays a core part in machine learning, it tells a machine what to do next in a structured manner. These are the following machine algorithms you must know and understand how do machine learning algorithm works.

  • Linear Regression

  • Logistic Regression

  • Support vector machine (SVM)

  • Random Forest

  • Decision Trees

  • K-Means

  • Gradient Descent

  • Naive Bayes

  • Dimensionality Reduction Algorithms

  • K-Nearest Neighbors (KNN)

[Step 4] Data Preprocessing

Data Preprocessing is a technique of transforming raw data into an understandable data format. That is, you need to pre-process the data before providing data sets to the machine learning algorithm. And you must know that how to do that.

[Step 5] Machine Learning Library

There are many machine learning library available for Python you can choose any of them. However, the most preferable libraries among AI Engineers are TensorFlow and scikit-learn.

You can learn the basics of these tools by visiting their official site, viz. https://www.tensorflow.org/tutorials/ & https://scikit-learn.org/stable/

Practice all the things you have learned so far in your chosen library.

[Step 6] Deep Learning

At this point, you have already gained knowledge of Python and Machine Learning and now it’s time to move on the next step of Artificial Intelligence career path which is Deep Learning.

Using TensorFlow start learning deep learning by solving some neural network problems.

You have learned all the required things which needed to become an AI Engineer. And now you need some real exposure which we will know in our next step.

[Step 7] Hone Your Skills

To hone your skills you can actually do many things such as…

  • Internship

  • Attend AI conferences and Tech Talks

  • The best way is to join the Artificial Intelligence Open Source community. For this, you can join GitHub community and participate on many ongoing projects. Here you will get hands-on experience on live projects.  100 Best GitHub Artificial Intelligence Projects. The more projects you do, the better you become.

  • Join Kaggle. Kaggle is a platform to get AI and data science projects. You can also participate in a competition and could win the prize.

[Step 8] AI Engineer

Now it’s time for searching jobs in the Artificial Intelligence domain. So prepare your best resume and start applying for the jobs. So these are the steps you will have to take if you want to get into the Artificial Intelligence career path and become an AI Engineer. To get more and better opportunities in Artificial Intelligence career path, you have to keep yourself up to date with the very latest technology because new fields keep emerging as the time passes by.

Artificial Intelligence Career Path Prerequisites

1. Bachelor’s degree in any of the subject is required. But, Bachelor’s in computer science got more preference than others.

2. Mathematics: You should have known about these topics

  • Calculus

  • Graph Theory

  • Statistics and Probability Theory

  • Linear Algebra

  • Matrices

  • Vectors

  • Functions

3. Languages: If you know multiple languages such as Chinese, Spanish, English, Hindi etc. is a plus.

4. Science: Physics and Biology [In biology, you should go for Psychology and Neuroscience]

5. The knowledge of Mechanical, Electrical and Electronics is a plus.

6. Programming Languages: Python, Java, C/C++, JavaScript, R, Scala, LISP, Prolog

Python is the most popular language in Artificial Intelligence technology. Therefore, Python is one of the most widely used programming languages. All credit goes to its simple syntax which is easy to learn and implement.

 
 

The Skills Required For An Artificial Engineer

A road-map towards becoming an AI engineer. AI is an emerging technology; most professionals are opting for AI certifications from some of the best online programs. Professionals looking towards a career in AI must have a closer look at the technical and business skills they need to master.

👉Technical Skills

  • Programming languages like R, Python, Java, and C++

Having a knack for coding is one important skill one needs to have as an AI engineer. Being proficient in programming languages like R, Python, Java, and C++ are important. This will help understand concepts like data structures and classes. Learning more than one programming language will always prove as an added advantage since organizations these days are focusing on individuals with multiple skill-set.

  • Statistics, Linear Algebra and Calculus

To understand how algorithms and machine learning functions it is important one understands statistics. Gaussian distributions, standard deviation, and mean are going to come up a lot. Solid understanding of probability and models like Naive Bayes, Hidden Markov models, and Gaussian Mixture models is advisable. Additionally, one needs to be familiar with vectors, matrices, matrix multiplication, and understanding of integrals and derivatives.

  • Natural Language Processing

NLP is an important sub-field of artificial intelligence, this enables computers to understand and process human languages. In short, the natural language processing gets the computer closer to a human-level understanding of language. This covers two major areas of work like linguistics and computer science, there are possibilities one might get to works with audio, video, or text. This is why is essential to have good control and working knowledge in libraries like NLTK and Gensim and techniques such as sentimental analysis, summarization, and word2vec.

  • Neural Network Architectures

When tasks get too complex and complicated for humans to code the role of machine learning comes to play. That being said, recent advances in machine learning have enabled computers to do tasks that are too impractical for the human mind to comprehend.

Neural networks have proven to be the precise way of approaching problems like speech recognition, image classification, and translation.

👉Non-Technical Skills

How else can an AI engineer explain the critical events to stakeholders and individuals with no technical know-how without having the non-technical skills? To become a successful AI engineer it is also important to have knowledge in the non-technical field.

  • Industry knowledge

In-depth knowledge regarding the industry scenario will prove beneficial for AI professionals working in this field since this will help professionals understand the real-world scenario and the pain points of the industry.

  • Communication Skills

Explaining concepts like artificial intelligence and machine learning can be tough if one does not communicate in simple language. Having good communication skills is going to be much easier when one needs to explain such concepts to individuals with very little technical knowledge.

  • Critical Thinking

Numbers and data should intrigue AI engineers, they must be able to use this finding and jump to insightful conclusions. With the emergence of AI, every sector is now looking to implement artificial intelligence making the demand for professionals 2x higher.

Career Opportunities in Artificial Intelligence

1. Big Data Engineer

The role of a Big Data Engineer is to create an ecosystem for the business systems to interact efficiently. Their primary task is to build and effectively administer big data of an organization. They also have to carry out the function of obtaining outcomes from big data in a robust manner.

Being a big data engineer will fetch you a good salary when compared to other AI roles. In Spark and Hadoop systems, a big data engineer deals with preparing, managing and establishing a big data environment. The role is suitable for those who are keen to play with new technical tools and can step above the relational database box. Programming languages like Python, R and Java are essential in building your career in AI as a big data engineer. Skills related to SQL and Apache Spark enhances your chances of grabbing the relevant career opportunity. Aspirants should have some proper insights on data migration along with data visualization and mining.

Applicants who have a PhD in the field of Computer Science or Mathematics are given more preferences. It is quite evident that one can flourish in their career in AI by being a Big Data Engineer

2. Business Intelligence Developer

The primary responsibility of a Business Intelligence Developer is to consider the business acumen along with AI. They recognize different business trends by assessing complicated data sets. They help in swelling the profits of a company by preparing, developing and nourishing business intelligence solutions.

Business profitability and efficiency are the two significant factors of development considered by them. They also assist in optimizing different processes and workflow across the organization. Their demands have intensified recently due to their capabilities in dealing with complicated data of cloud-based platforms. One who is aware of computer programming and data sets can acquire this position. A formal bachelor’s degree in the field of computers, mathematics or engineering is suitable to land you in a job. The problem-solving strengths and analytical capabilities of the applicants should be high.

Sound knowledge of SQL servers and queries along with data warehouse designing is needed while pursuing this career option. The role also pays quite well and their demands won’t diminish in the near future. This makes it one of the major AI career opportunities.

3. Data Scientist

Data scientists assist in gathering relevant data from multiple sources for the purpose of assessing it to gain constructive inferences. The inferences gained are influential in tackling various issues concerned with the business. Depending upon different data patterns, past and present information, data scientists make various predictions.

The performance of the business is positively impacted due to a data scientist. Job-seekers are required to be equipped with modern tools like Spark, Hadoop, Pig or Hive while pursuing this career option. The candidate must be comfortable using programming languages like Python, Scala or SQL.

In terms of education, the applicant should hold a master’s degree in Mathematics or Computer Science. Any advanced degree is likely to increase the chance of getting the job. Significant experience in the field of machine learning is mandatory. To interact with the managers, data scientists should possess sound communication skills. Their analytical abilities should be outstanding. Many large technical firms need data scientists to carry out important tasks for the growth of the company. A career in AI thus opens the door to be a Data Scientist.

4. Machine Learning Engineer

Machine learning engineers are involved in building and maintaining self-running software that facilitates machine learning initiatives. They are in continuous demand by the companies and their position rarely remains vacant. They work with huge chunks of data and possess extraordinary data management traits.

They work in the areas of image and speech recognition, prevention of frauds, customer insights, and management of risks. To become a machine learning engineer, one must have sound command in applying predictive models dealing with magnificent data.  Programming, computing, and mathematics are essential to becoming successful as a machine learning engineer.

A master’s degree in Mathematics or Computer Science is preferred. Python, R, Scala, and Java are the required technology stacks. Having in-depth knowledge about machine learning algorithms, neural networks and deep learning is highly beneficial. Sound familiarity with software development tools, cloud applications, and excelling coding skills gives you an added advantage. 

5. Research Scientist

Research scientists undertake efforts in performing extensive research dealing with applications of machine learning and machine intelligence. A research scientist is one who has gained expertise in the field of applied mathematics, statistics, deep learning, and machine learning.

Applicants are expected to have a PhD degree or advanced master’s degree in mathematics or computer science. The salary of a research scientist is quite high and organizations recruit those who have a good experience in their AI career. Significant knowledge of Natural Language Processing (NLP) and Reinforcement Learning is essential while applying for the role. Aspirants who possess insights regarding parallel computing, computer perception, benchmarking, graphical models, and distributed computing are favoured. It is quite evident that the importance of research scientists will not diminish in the next generation.

6. AI Data Analyst

The major function of an AI data analyst is to perform data mining, data cleaning, and data interpretation. By cleaning data, requisite data is collected to carry out data interpretation. Any sort of useless data is discarded by them so that it does not hamper the data interpretation process. 

With the help of statistical tools and methods, inferences are drawn from the data by an AI data analyst. To become an AI data analyst, you are required to possess a bachelor’s degree in mathematics or computer science.

A comprehensive understanding of regression and the ability to utilize MS Excel is essential to acquire this position. In comparison to other AI roles, the salary is quite low for an AI data analyst. The demand for AI data analysts is quite stable but their future cannot be ascertained.

7. Product Manager

In the field of AI, the duty of a product manager is to resolve challenging problems by strategically collecting data. You should possess the skill of identifying relevant problems that obstructs the business proceedings. The next step is to get hold of related data sets to facilitate data interpretation.

After data interpretation, the product manager is required to estimate business impacts from the outcomes of data interpretation. Each organization needs a product manager, whose demands have skyrocketed these days.

8. AI Engineer

AI engineers are problem-solvers who develop, test and apply different models of Artificial Intelligence. They effectively handle AI infrastructure. They make use of machine learning algorithms and understanding of the neural network to develop useful AI models.

One can get business insights with these models and this helps the company to make effective business decisions. Undergraduate or postgraduate degrees in the field of data science, computer science or statistics are mandatory. Any kind of certifications on ML or data science adds to the advantage.

Proficiency in programming languages, such as Python, R or C++, is essential. The applicants should have a strong grasp in statistics, NLP, applied mathematics, and analytics. The pay scale of an AI engineer is quite good due to the role’s surging demands.

9. Robotics Scientist

A reduction in jobs will indeed take place due to the emergence of robotics in the field of AI. Conversely, jobs will also rise as robotics scientists are in incessant demands by major industries for programming their machines. The robots will help in carrying out certain tasks efficiently.

The candidate should have a master’s degree in robotics, computer science or engineering. The median salary for a robotics scientist is quite high. Although automation is favoured by robots, there should be some professionals to build them. Thus, the risk of losing jobs is minimized.

 
 

The Skills Required For An Artificial Engineer

A road-map towards becoming an AI engineer. AI is an emerging technology; most professionals are opting for AI certifications from some of the best online programs. Professionals looking towards a career in AI must have a closer look at the technical and business skills they need to master.

👉Technical Skills

  • Programming languages like R, Python, Java, and C++

Having a knack for coding is one important skill one needs to have as an AI engineer. Being proficient in programming languages like R, Python, Java, and C++ are important. This will help understand concepts like data structures and classes. Learning more than one programming language will always prove as an added advantage since organizations these days are focusing on individuals with multiple skill-set.

  • Statistics, Linear Algebra and Calculus

To understand how algorithms and machine learning functions it is important one understands statistics. Gaussian distributions, standard deviation, and mean are going to come up a lot. Solid understanding of probability and models like Naive Bayes, Hidden Markov models, and Gaussian Mixture models is advisable. Additionally, one needs to be familiar with vectors, matrices, matrix multiplication, and understanding of integrals and derivatives.

  • Natural Language Processing

NLP is an important sub-field of artificial intelligence, this enables computers to understand and process human languages. In short, the natural language processing gets the computer closer to a human-level understanding of language. This covers two major areas of work like linguistics and computer science, there are possibilities one might get to works with audio, video, or text. This is why is essential to have good control and working knowledge in libraries like NLTK and Gensim and techniques such as sentimental analysis, summarization, and word2vec.

  • Neural Network Architectures

When tasks get too complex and complicated for humans to code the role of machine learning comes to play. That being said, recent advances in machine learning have enabled computers to do tasks that are too impractical for the human mind to comprehend.

Neural networks have proven to be the precise way of approaching problems like speech recognition, image classification, and translation.

👉Non-Technical Skills

How else can an AI engineer explain the critical events to stakeholders and individuals with no technical know-how without having the non-technical skills? To become a successful AI engineer it is also important to have knowledge in the non-technical field.

  • Industry knowledge

In-depth knowledge regarding the industry scenario will prove beneficial for AI professionals working in this field since this will help professionals understand the real-world scenario and the pain points of the industry.

  • Communication Skills

Explaining concepts like artificial intelligence and machine learning can be tough if one does not communicate in simple language. Having good communication skills is going to be much easier when one needs to explain such concepts to individuals with very little technical knowledge.

  • Critical Thinking

Numbers and data should intrigue AI engineers, they must be able to use this finding and jump to insightful conclusions. With the emergence of AI, every sector is now looking to implement artificial intelligence making the demand for professionals 2x higher.

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