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Existing in a world endangered by threats like DDoS attacks and ransomware attacks, global businesses continually looking at their target customers’ interest. 

The Internet of Things (IoT) has left consumers asking for more as far as technological advancements are concerned. The consistent demand for technology-studded solutions has created a massive opportunity for individuals to have a rewarding career in Artificial Intelligence. Especially in the wake of rising ransomware attacks and DDoS attempts, the need for highly qualified and top-skilled professionals is always on the rise. 

Let’s first get to know what Artificial Intelligence is all about. 

What is Artificial Intelligence?

Artificial intelligence is all about enabling machines and gadgets to perform their functions, just like humans. The consistent rise in technology has further augmented the importance of artificial intelligence and machine learning. The rise of smart homes has also highlighted that humans are looking to rely more than ever on machines to get their daily tasks done. Be it robots, self-driving cars, chatbots, digital assistants, or intelligent machines. Our lifestyle is getting a complete makeover as we travel in time. It has been believed that the internet is the biggest revolution in human history.

However, it will soon be replaced by machine learning and deep learning. If you speak with the decision-makers at Silicon Valley, you will learn that they are eagerly anticipating the rise of deep learning and machine learning for the years to come. The idea is to make lives more comfortable and reduce the chances of human error. 

Despite all the hype about artificial intelligence, the Internet of Things, machine learning, and deep learning, there has been a concern about how robots are likely to replace humans. 

One of the most significant talking points in the world right now is whether we will lose our jobs to robots in the future or not. However, the truth is that machines’ rise will only create more jobs for humans, rather than cutting them down. 

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How the Job Market Shapes Up Amid the Rise of AI?

As mentioned above, the world has recently witnessed a sharp rise in DDoS attacks and ransomware attacks, especially in the Novel Coronavirus pandemic aftermath. 

Although around 33 million Americans applied for unemployment benefits since the global outbreak of COVID-19, experts believe that the coming years will see an increase in the number of available jobs, thanks to the rise of artificial intelligence. 

Let’s look at how professionals can keep up their career growth amid the rise of Artificial Intelligence. According to Gartner’s study in 2017, AI is estimated to create around 2.3 million new jobs for humans by the end of the Year 2020. The good news is that almost all the organizations across all sectors have already started embracing Artificial Intelligence and Machine Learning. 

It only means that professionals with skills in these disciplines are set to record phenomenal growth in their career. The pandemic might have eaten up millions of jobs, but it hasn’t deterred global companies from saying welcome to AI and IoT. 

If you are motivated enough to grow your career in Artificial Intelligence, here is your ultimate guide. 

The field is evolving itself into the next big thing. An AI engineer’s job responsibilities should include:

  1. Researching machine learning algorithms.
  2. Develop machine learning applications. 
  3. Transform data science prototypes.
  4. Manage teams of electrical engineers and robotics.
  5. Conduct machine learning testing.
  6. Create modules to train systems, if and when required.
  7. Select the right datasets as well as data representation techniques.

Job responsibilities might depend on AI engineers’ experience level, but they are mostly required to carry out the tasks mentioned earlier in their job.

Let’s look at the five essential skills every AI Engineer should possess to ensure a rewarding career. 

  1. Computer Science Fundamentals and Programming.
  2. Probability and Statistics.
  3. Data Modelling and Evaluation.
  4. Applying Machine Learning Algorithms and Libraries.
  5. Software Engineering and System Design.

1. Computer Science Fundamentals and Programming

The fundamentals of computer science and programming hold a crucial position in the field of Artificial Intelligence. 

Some of the most critical computer science fundamentals include:

  • Data structures (multi-dimensional arrays, trees, graphs, stacks, and queues).
  • Computability (P vs. NP, big-O notation.
  • NP-complete problems, and approximate algorithms). Algorithms (search, sort, optimize, dynamic programming, etc.), and 
  • Computer architecture (cache, deadlocks, memory, bandwidth, and distributed processing). 

As an AI engineer, you should be able to formulate, implement, adapt, or address them at the programming level. You can use coding competitions, practice problems, and hackathons to polish your skills in this field. 

2. Probability and Statistics

An AI engineer should have a complete grasp of the following topics and techniques. 

  1. Conditional Probability.
  2. Likelihood.
  3. Bayes Rule.
  4. Independence.
  5. Bayes Nets.
  6. Hidden Markov Model.
  7. Markov Decision Processes.

These topics and techniques lie at the core of machine learning algorithms and help AI engineers to deal with the troubles in this field. These topics are very much relevant to Statistics, which uses measures like mean, median, variance; distributions like uniform, normal, binormal, Poisson; and analysis methods like hypothesis testing and ANOVA to build and validate models by taking data into stock. The truth of the matter is that a number of machine learning algorithms have been derived from statistical modeling procedures.

3. Data Modeling and Evaluation

Using data and numbers to device models is nothing less than an art. Engineers use data to estimate the underlying structure of a dataset and find useful patterns, which are generally a combination of correlations, clusters, eigenvectors or properties of classification, regression, anomaly detection, etc. The aim is to evaluate the practicality of a given model. Engineers also keep an eye on the errors to tweak the model by suggesting and trying various different options and algorithms. 

4. Applying Machine Learning Algorithms and Libraries

Although standard machine learning implementation algorithms are easily available, their application choosing the right model requires someone who has sheer knowledge on the subject. No layman can pull it off on any given day. Therefore, this field requires learned artificial intelligence professionals who know exactly what Spark MLlib, scikit-learn, H2O, Theano, and TensorFlow are.

Moreover, these professionals also know all about how to use the right application model, including decision tree, neural net, nearest neighbor, an ensemble of multiple models, and support vector machines. As an AI Engineer, you should be fully aware and educated about the different pros and cons of different strategies. 

5. Software Engineering and System Design

An AI engineer is expected to deliver software that is generally a meager part of the bigger picture. That bigger picture is generally a huge ecosystem of services and products that a company offers to its clients or uses for its functionality. AT the end of the day, you need to build a carefully designed system that supersedes bottlenecks and provide it with enough strength to deal with the increased volume of data.

As an AI engineer, you should keep a focus on maintaining the best practices, including system design, system requirements analysis, system documentation, system testing, system modularity, and version control. 

Artificial Intelligence

Leading Jobs in AI Sector

As you know the basic skills you need to become an AI engineer, now it is time to see which jobs are available in the field of Artificial Intelligence. As a matter of fact, today’s businesses are more willing than ever to invest in machine learning and hire the best available talent. Some of the leading AI-related jobs are Data Scientist, Machine Learning Engineer, Research Scientist, AI Engineer, and Business Intelligence Developer. Of the above-mentioned jobs, Artificial Intelligence Engineer is the most common one. 

Let’s have a detailed look at the top five roles in your AI career path

1. Machine Learning Engineer

Generally speaking, machine learning engineers are the professionals who program machines and give them the power to perform certain specific tasks. Their core responsibility is to create as well as manage machine learning platforms. Professionals with a strong academic background in engineering and programming are the best-suited ones for this role. The Machine Learning Engineers are also required to have a good bit of skills with different programming languages, including Java, Python, and Scala. 

Moreover, ML Engineers should also be well-versed with different methodologies and strategies to handle large chunks of data. The knowledge of SQL and R can go a long way in this regard. 

If you are seeking a career as an ML Engineer, you should be accustomed to applying different predictive models by keeping the best practices intact. It can be nothing less than gold if you have a few years of experience in agile development and leading development tools, such as Eclipse and IntelliJ. You should have a master’s degree or a doctoral degree in Computer Science or Mathematics while decent skills with computer programming and analytics will always give you a great chance of excelling as a Machine Learning Engineer.

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2. Data Scientist

Data Scientists are highly regarded in the field of Artificial Intelligence. Their core job is to collect, analyze, and interpret large and complex data by utilizing different predictive analytics and machine learning tools. At an advanced level, their job role also involves the implementation of new machine learning models that allows enterprises to make timely decisions. 

Data Scientists develop algorithms to collect data and refine it for different uses. This way, they make it easier to create data workflows that allow the predictive models to paint a clearer picture of the organization.

If you’re looking to excel as a Data Scientist, you should have a good bit of knowledge about Hive, Hadoop, MapReduce, Pig, Spark, and various other similar tools. Moreover, data scientists should also possess decent skills in different programming languages, including Scala, Python, Java, Perl, and SQL. You should hold a master’s or a doctoral degree in Computer Science to seek a rewarding career as a Data Scientist. Recruiters generally like to hire data scientists with at least two years of prior experience in their respective fields.

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3. Business Intelligence Developer

Business intelligence developers are highly sought after by today’s businesses and enterprises. They are tasked with the analysis of complex data to identify different evolving markets and business trends. They are the professionals who make sure that a business’s bottom-line is in the green range. They not only design models and designs but also maintain them along with complex business data in highly-accessible cloud-based data platforms

The Business Intelligence Developers are required to monitor the transition of a traditional business into the one that relies heavily on market data. If you have strong technical and analytical skills, you can excel as a Business Intelligence Developer. You should hold a bachelor’s degree in Computer Science or Engineering. Besides, you should also possess decent experience in data mining, BI technologies, SQL queries, and data warehouse designs.

4. Research Scientist

Research scientists are generally positioned in the top-tier management of any company. Since they have master’s degrees in multiple AI disciplines (Machine Learning, Deep Learning, Applied Mathematics, and Computational Statistics), they are expected to have strong knowledge about computer perception, reinforcement learning, graphic models, and natural language processing. 

If you are seeking a career as a Research Scientist, you should hold a master’s or a doctoral degree in Computer Science. An advanced degree in a related field can go a long way in ensuring a robust career path. As a Research Scientist, you should have a strong knowledge of parallel computing, distributed computing, artificial intelligence, and machine learning. 

5. Big Data Engineer

As the title suggests, Big Data Engineer is tasked to deal with huge chunks of business and market data sets. Their core job is to design, plan, and develop big data environments on Hadoop and Spark platforms. They are on top of the development of an ecosystem that allows different business systems to interact with each other and share relevant data. 

As a Big Data Engineer, it is your job to handle how data flows in an organization. The idea is to make sure that the data reaches where it is intended to end. You should hold a Ph.D. in Computer Science or Mathematics to make a name as a Big Data Engineer. Moreover, additional certifications in various programming languages (Python, C++, Java, and Scala) as well as deep knowledge about data mining, data visualization, and data migration also come in handy. 

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ITIL Certification-Hadoop certification - Certifications To Boost Your Job Search - IT Certifications-Pre-employment Requirements

List of Artificial Intelligence Certifications

You can enroll yourself in the following certifications to give your AI career a kick-start. 

  1. Learn with Google AI.
  2. Stanford University Machine Learning.
  3. Google – Machine Learning.
  4. Columbia University Machine Learning.
  5. Nvidia – Fundamentals of Deep Learning for Computer Vision.
  6. MIT – Professional Certificate Program in Machine Learning and Artificial Intelligence.


The field of artificial intelligence and machine learning is still in its early phase, which means it is evolving all the time. With so many new trends shaping up regularly, it is imperative for AI professionals to keep abreast of the latest trends and advancements in the field. The best way of keeping yourself aligned with the latest advancements is by attending events and conferences, signing up with the newsletters of leading AI-specific websites and forums (Google AI, DeepMind, etc.).

The field of artificial intelligence has set no boundaries yet with regards to what the future may offer in the years to come. From smartwatches to chatbots and AI-powered robots, artificial intelligence companies are working days and nights to turn science fiction into reality. The sky’s the limit when it comes to starting and growing a career in the field of artificial intelligence.

If you want to pursue a career in AI, you should start planning today and make sure that you have covered all the bases before finally venturing into this field. 

Written By
Sama is a blogger and freelance writer. She writes a knowledge base and informational blogs for Blockdos. She mostly writes about cybersecurity and tech hacks. Her vast knowledge of web security makes all her blogs informative and worth reading providing her readers with tips and hacks that work!

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