10 High-Paying Jobs in Artificial Intelligence (AI)

Recently, artificial intelligence (AI) has opened up several possibilities for businesses. It has made several imponderables easier and possible. From enhancing customer interaction to leveraging the knowledge in medical diagnosis, it is making things easier.  Artificial intelligence is a new and niche field. It is the simulation of human intelligence processes by machines, especially computer systems. 

AI careers have been growing exponentially and the need for more qualified people in this space has opened up many high-paying careers. According to the International Data Corporation (IDC), the AI market in India is projected to grow at a CAGR (Compound Annual Growth Rate) of 20.2 per cent to reach $ 7.8 billion by 2025 from $ 3.1 billion in 2020. According to the World Economic Forum, AI and machine learning specialists are second on the list of jobs with increasing demand. In this blog post, we explore ten different AI careers you can pursue.

AI Careers that are sought after

Within AI, there are many jobs needing specific skills and experience. These are the top ten.

1. Machine Learning Engineer

A machine learning engineer (ML engineer) is a person who researches, builds and designs self-running artificial intelligence (AI) systems to create production-ready scalable data science models that can handle terabytes of real-time data. Machine learning engineers are at the intersection of software engineering and data science, leveraging big data tools and programming frameworks.

A machine learning engineer is required to have an advanced knowledge of mathematics, programming and data science to aid her recognition of different types of data sets and be able to define patterns in data. Machine learning engineers evaluate data streams and determine how best to go about producing models that churn out requirements that meet an organization’s needs.

2. Data Scientist

According to Wikipedia, “Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured, data and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.”

A data scientist is responsible for collecting, analyzing and interpreting extremely vast data. The data scientist’s role combines these three responsibilities. It draws upon the skills needed for various traditional technical roles like that of a mathematician, scientist, statistician and computer engineer.

Data science combines knowledge of mathematics, computer science and domain expertise. This includes statistical research, machine learning and data processing. The qualifications of a data scientist can include:

  •   An advanced degree in statistics, mathematics and computer science.
  •   Statistical analysis and the understanding of unstructured data.
  • Experience with cloud tools like Amazon S3 and the Hadoop platform.
  • Programming skills with Python, Perl, Scala and SQL.
  • Working knowledge of Hive, Hadoop, MapReduce, Pig and Spark.

3. Business Intelligence (BI) Developer

A business intelligence developer generates, organizes, and maintains business interfaces. They are responsible for dashboards, data visualizations, data querying tools for users to get the information. A business intelligence developer is an engineer who uses software to interpret and display data for an organization. They create tools or troubleshoot methods being used to improve the company’s research process. The skills of a BI developer include:

  • Experience with BI tools.
  • Data analysis background and business analysis skills.
  • Bachelor’s degree in engineering, computer science.
  • Hands-on experience in data warehouse design, data mining and SQL.

·  Knowledge of BI technologies like Tableau, Power BI.

4. Research Scientist

A research scientist’s role is a very academically-driven one in the AI space. Individuals in this role are expected to be recognized experts in identified research areas such as artificial intelligence, machine learning, computational statistics and applied mathematics. These include areas such as deep learning, graphical models, reinforcement learning, computer perception, natural language processing and data representation.

They lead research to advance the science and technology of intelligent machines, enable learning the semantics of data (images, video, text, audio, speech and other modalities) and devise better data-driven models of human behaviour.  

Their qualifications are mostly PhD and publications in machine learning, AI, computer science, statistics, applied mathematics, data science or related technical fields.

5. Big Data Engineer/Architect

A big data architect normally solves problems that are quite big by analyzing the data, using Hadoop and Spark systems. A database on a large scale needs to be handled in order to analyze the data. This assists with making informed business decisions. Data architects can work in nearly any industry like technology, entertainment, healthcare, finance and retail. Technical data architect skills include:

  • Data mining and data management. 
  • Coding languages like Python and Java to develop applications for data analysis.
  • Machine learning to build scalable systems for handling big data.
  • Structured query language (SQL) to manipulate data.
  • Data modeling tools like ERWin or Visio to visualize metadata and database schema.

6. AI Software Engineer

AI engineering is a field of research and practice. It combines the principles of systems engineering, software engineering, computer science and human-centered design to create AI systems in accordance with human needs for mission outcomes. The main role of an AI software engineer is to productize the data science work and make it work for customers who are both internal and external.  The AI engineer must collaborate with the data scientists, data architects and business analysts.

AI engineers require a bachelor’s degree in IT, computer science, statistics or data science and a master’s or PhD in one of these disciplines. An AI engineer should demonstrate programming language proficiency in one or more of common computer languages of Python, Java and C++.

7. Software Architect

A software engineer who is responsible for high-level design choices of overall system structure and behavior is called a software architect. Their job is to create and maintain AI architecture, plan and implement solutions, choose the toolkit, and ensure a smooth data flow. A software architect needs broad and deep technical knowledge in order to be effective. They are normally required to have a master’s degree in computer science. They may also require experience with cloud platforms, data processes, software development and statistical analysis.

8. Data Analyst

The key responsibilities of a data analyst include designing and maintaining data systems and databases, mining data from primary and secondary sources and using statistical tools to interpret data sets. They are also responsible for preparing reports for the executive leadership covering trends, patterns and predictions using data. Essentially, a data analyst cleans and models data, interprets and then presents the data in the form of reports. A data analyst needs to have a bachelor’s degree and a master’s in either of the following: data science, applied math or statistics, computer science, economics or finance.

9. Robotics Engineer

Robotics engineering requires you to be proficient in many technical fields. The robotics engineer acts as the bridge between mechanics, electronics, computer science and cognitive psychology. Robotics engineers design the plans and processes needed to build robots. In essence, they construct, configure, test and debug robots and robotic systems. They ensure that robotic machines operate safely, dependably and with precision. They are required to have a bachelor’s degree in robotics-related disciplines like mechanical engineering, electrical engineering, computer science or mathematics. You are also expected to know computer-aided design/manufacturing, scripting tools, IoT, artificial Intelligence (AI)/ machine learning (ML).

10. NLP Engineer

NLP (neuro-linguistic programming) engineers create devices and systems that can understand the human language. They use these linguistic tools to engineer computers that perform useful tasks involving human language. NLP Engineers are responsible for the programming behind technology’s ability to process and analyze natural language data. For instance, this is how Google or Alexa can understand what you are saying. Mostly they are required to have a bachelor’s degree in engineering, data science or computer science. A master’s degree in data science or artificial intelligence or a PhD with a focus in NLP is likely to be preferred or required for high-level positions.


Artificial intelligence is an exciting field that will affect everything in our lives. From the way we entertain ourselves to the way we learn to how we buy and to how we receive healthcare. It will change life dramatically and people will not be required to perform dangerous or risky tasks. AI may replace humans in doing arduous or unhealthy jobs. AI and machine learning jobs have jumped by almost 75 percent over the past four years and are poised to keep growing.

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