How to Become an Artificial Intelligence (AI) Engineer in India (2024)

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Yachana Sharma

27 June 2024

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How to Become an Artificial Intelligence (AI) Engineer in India (2024)

Learn how to become an AI Engineer in India and what are the top skills to unlock potential career opportunities in artificial intelligence.

Features

Table of Contents

  • What is Artificial Intelligence (AI) Engineering?

  • What Does an Artificial Intelligence Engineer Do?

  • How to Become an Artificial Intelligence (AI) Engineer in 2024?

  • Top Courses to Enter the Artificial Intelligence Field

  • Artificial Intelligence (AI) Jobs with Salaries (US, UK & India)

  • Final Thoughts

Learn how to become an AI Engineer in India and what are the top skills to unlock potential career opportunities in artificial intelligence.

What is Artificial Intelligence (AI) Engineering?

Artificial Intelligence (AI) Engineering focuses on deploying AI applications and systems. AI systems aim to imitate cognitive processes such as perception, reasoning, learning, problem-solving, and decision-making. These systems can analyze vast amounts of data to identify patterns. The AI systems then make predictions or recommendations based on the information they process.

Artificial Engineering uses computer science, machine learning, data science, and software engineering. It requires a deep understanding of AI principles and programming languages and AI industry-specific knowledge to solve complex problems.

Artificial Intelligence (AI) Engineering focuses on deploying AI applications and systems. AI systems aim to imitate cognitive processes such as perception, reasoning, learning, problem-solving, and decision-making. These systems can analyze vast amounts of data to identify patterns. The AI systems then make predictions or recommendations based on the information they process.

Artificial Engineering uses computer science, machine learning, data science, and software engineering. It requires a deep understanding of AI principles and programming languages and AI industry-specific knowledge to solve complex problems.

What Does an Artificial Intelligence Engineer Do?

An Artificial Intelligence Engineer is responsible for:

  • Designing and building smart algorithms and models,
  • Developing machine learning channels,
  • Optimizing AI systems for performance and scalability,
  • Merging AI capabilities into software systems and creating intelligent applications,
  • Ensuring the moral usage of AI technologies.

A career in AI Engineering leads to various job opportunities in Artificial Intelligence because of the strong skillset that it demands. Further, we’ll uncover how to become an AI Engineer in 2024.

An Artificial Intelligence Engineer is responsible for:

  • Designing and building smart algorithms and models,
  • Developing machine learning channels,
  • Optimizing AI systems for performance and scalability,
  • Merging AI capabilities into software systems and creating intelligent applications,
  • Ensuring the moral usage of AI technologies.

A career in AI Engineering leads to various job opportunities in Artificial Intelligence because of the strong skillset that it demands. Further, we’ll uncover how to become an AI Engineer in 2024.

How to Become an Artificial Intelligence (AI) Engineer in 2024?

To become an Artificial Intelligence (AI) Engineer in 2024, you'll need a combination of education, practical experience, and skills. Leverage the below-mentioned steps to pursue your career:

Education

Earn a bachelor's in computer science, data science, or a related field. Build a powerful background in mathematics and statistics.
 

Acquire Artificial Intelligence Engineer Skills

AI Engineers are instrumental in this era in creating intelligent systems, machine learning algorithms, and data-driven solutions. Their expertise in programming, data analysis and deep learning is vital for organizations. 

Businesses leverage AI's potential and gain a competitive edge. So to thrive in this domain, artificial engineers must have the below listed top AI engineer skills:
 

  • Soft Skills
    Here are the soft skills you must master before entering the AI industry: 

    • Communication: You will collaborate with data scientists and analysts when working in the AI industry. AI engineers require strong communication skills. 
       
    • Analytical Thinking: Users might face issues when using AI apps and programs. As an AI engineer, you must think analytically to understand their issue and find an appropriate solution.
       
    • Decision-Making: Every field, even AI, has risks. As an AI engineer, working with a team requires decision-making that can maximize productivity and make your project successful. To have strong decision-making skills, it is important to have focus, an eye for detail, and a sharp mind. 
       
    • Collaboration: AI engineers work in teams and must work together efficiently. Collecting and conveying information to the team is required to make that possible. You must have good collaboration skills to join and help the team work together.
       
    • Problem-Solving and Critical Thinking: Artificial Intelligence Engineers need excellent problem-solving and critical-thinking abilities to tackle complex AI challenges. They should be adept at breaking down problems, analyzing data, and designing innovative AI solutions. Robust analytical skills, creativity, and the ability to think outside the box are key traits for successful artificial engineers.
       
  • Technical Skills
    As an AI engineer, you must have the following technical skills:

    • Programming Languages: Have a sound knowledge of programming languages like Python, Java, C++, etc., before you enter the AI field.

      Python: It is a widely used language in AI because of its faster execution, simplicity, and code reliability, which makes it a recession-proof technical skill. It helps in writing complex algorithms with minimal coding.

      Java: It is also used widely in AI for implementing programming, neural networks, search algorithms, etc.
       
      C++: It is used in AI to develop operating systems, video games, etc. C++ is a flexible language highly useful in AI.
       
    • Data Science and Mathematics: To become a successful AI engineer, professionals can enroll in Data Science certifications and courses are a must to have a solid data science foundation. They should also have a grasp of math skills for data science. Data science helps create and train artificial intelligence models to understand data and make predictions or perform tasks. You can progress in your career and become a Senior Data Scientist with the correct skills. 
       
    • Machine Learning (ML): Artificial engineers need a solid understanding of machine learning trends. ML algorithms learn from examples and improve their performance over time. By leveraging ML, AI Engineers can develop intelligent systems that recognize patterns, understand language, make recommendations, and solve complex problems.
       
    • Neural Networks and Deep Learning: Deep Learning involves creating and training neural networks, which are algorithms inspired by the human brain. Deep learning models process vast amounts of data to extract meaningful patterns. Expertise in deep learning and neural networks is pivotal in AI. Understanding architectures like convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) is vital for solving complex AI problems.
       
    • Natural Language Processing (NLP): NLP enables machines to understand and interpret human language. Proficiency in NLP will help you develop algorithms that enable computers to interpret and respond to human languages, like chatbots or voice assistants. It will also help you make a career in Natural Language Processing.
       
    • Big Data and Distributed Computing: Dealing with large datasets is common in AI projects. Understanding distributed computing frameworks like Apache HBase and Apache Spark, as well as cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP), is essential for efficient processing.
       
    • Reinforcement Learning: This branch of AI involves training agents to make sequential decisions based on rewards and punishments. Familiarity with reinforcement learning algorithms will help you teach machines to learn and improve their decision-making abilities.
       
    • Computer Vision: Machines utilize computer vision to perceive and decode visual information from images or videos. Using techniques such as image recognition and object detection, computer vision allows machines to recognize objects, identify patterns, and make decisions based on visual input.you can enroll into Computer Vision courses to make a career in this field.
       
    • Shell Scripting: A computer program run by a command-line interpreter called Unix Shell. It is a vital component of AI for pattern recognition. Most AI data processing occurs on Linux-based machines, so you must know about UNIX tools, operations, and commands like SSH, grep, find, awk, etc.
       
    • Software Engineering: Strong software engineering skills are necessary for developing robust and scalable AI applications. Proficiency in software development practices, version control systems like Git, and software testing methodologies is crucial for building production-ready AI systems.
       

Keep up with the latest developments:

Stay updated with Al's latest advancements, research papers, and industry trends. Pursue online courses, like AI Chatbot courses and join AI communities.
 

Build a portfolio of AI projects:

Create your own AI projects to demonstrate your skills and knowledge. This could involve solving real-world problems using machine learning or developing AI applications.
 

Continuously learn and adapt:

Stay curious, explore new techniques, and continue refining your skills to keep pace with advancements in AI technology.

To become an Artificial Intelligence (AI) Engineer in 2024, you'll need a combination of education, practical experience, and skills. Leverage the below-mentioned steps to pursue your career:

Education

Earn a bachelor's in computer science, data science, or a related field. Build a powerful background in mathematics and statistics.
 

Acquire Artificial Intelligence Engineer Skills

AI Engineers are instrumental in this era in creating intelligent systems, machine learning algorithms, and data-driven solutions. Their expertise in programming, data analysis and deep learning is vital for organizations. 

Businesses leverage AI's potential and gain a competitive edge. So to thrive in this domain, artificial engineers must have the below listed top AI engineer skills:
 

  • Soft Skills
    Here are the soft skills you must master before entering the AI industry: 

    • Communication: You will collaborate with data scientists and analysts when working in the AI industry. AI engineers require strong communication skills. 
       
    • Analytical Thinking: Users might face issues when using AI apps and programs. As an AI engineer, you must think analytically to understand their issue and find an appropriate solution.
       
    • Decision-Making: Every field, even AI, has risks. As an AI engineer, working with a team requires decision-making that can maximize productivity and make your project successful. To have strong decision-making skills, it is important to have focus, an eye for detail, and a sharp mind. 
       
    • Collaboration: AI engineers work in teams and must work together efficiently. Collecting and conveying information to the team is required to make that possible. You must have good collaboration skills to join and help the team work together.
       
    • Problem-Solving and Critical Thinking: Artificial Intelligence Engineers need excellent problem-solving and critical-thinking abilities to tackle complex AI challenges. They should be adept at breaking down problems, analyzing data, and designing innovative AI solutions. Robust analytical skills, creativity, and the ability to think outside the box are key traits for successful artificial engineers.
       
  • Technical Skills
    As an AI engineer, you must have the following technical skills:

    • Programming Languages: Have a sound knowledge of programming languages like Python, Java, C++, etc., before you enter the AI field.

      Python: It is a widely used language in AI because of its faster execution, simplicity, and code reliability, which makes it a recession-proof technical skill. It helps in writing complex algorithms with minimal coding.

      Java: It is also used widely in AI for implementing programming, neural networks, search algorithms, etc.
       
      C++: It is used in AI to develop operating systems, video games, etc. C++ is a flexible language highly useful in AI.
       
    • Data Science and Mathematics: To become a successful AI engineer, professionals can enroll in Data Science certifications and courses are a must to have a solid data science foundation. They should also have a grasp of math skills for data science. Data science helps create and train artificial intelligence models to understand data and make predictions or perform tasks. You can progress in your career and become a Senior Data Scientist with the correct skills. 
       
    • Machine Learning (ML): Artificial engineers need a solid understanding of machine learning trends. ML algorithms learn from examples and improve their performance over time. By leveraging ML, AI Engineers can develop intelligent systems that recognize patterns, understand language, make recommendations, and solve complex problems.
       
    • Neural Networks and Deep Learning: Deep Learning involves creating and training neural networks, which are algorithms inspired by the human brain. Deep learning models process vast amounts of data to extract meaningful patterns. Expertise in deep learning and neural networks is pivotal in AI. Understanding architectures like convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) is vital for solving complex AI problems.
       
    • Natural Language Processing (NLP): NLP enables machines to understand and interpret human language. Proficiency in NLP will help you develop algorithms that enable computers to interpret and respond to human languages, like chatbots or voice assistants. It will also help you make a career in Natural Language Processing.
       
    • Big Data and Distributed Computing: Dealing with large datasets is common in AI projects. Understanding distributed computing frameworks like Apache HBase and Apache Spark, as well as cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP), is essential for efficient processing.
       
    • Reinforcement Learning: This branch of AI involves training agents to make sequential decisions based on rewards and punishments. Familiarity with reinforcement learning algorithms will help you teach machines to learn and improve their decision-making abilities.
       
    • Computer Vision: Machines utilize computer vision to perceive and decode visual information from images or videos. Using techniques such as image recognition and object detection, computer vision allows machines to recognize objects, identify patterns, and make decisions based on visual input.you can enroll into Computer Vision courses to make a career in this field.
       
    • Shell Scripting: A computer program run by a command-line interpreter called Unix Shell. It is a vital component of AI for pattern recognition. Most AI data processing occurs on Linux-based machines, so you must know about UNIX tools, operations, and commands like SSH, grep, find, awk, etc.
       
    • Software Engineering: Strong software engineering skills are necessary for developing robust and scalable AI applications. Proficiency in software development practices, version control systems like Git, and software testing methodologies is crucial for building production-ready AI systems.
       

Keep up with the latest developments:

Stay updated with Al's latest advancements, research papers, and industry trends. Pursue online courses, like AI Chatbot courses and join AI communities.
 

Build a portfolio of AI projects:

Create your own AI projects to demonstrate your skills and knowledge. This could involve solving real-world problems using machine learning or developing AI applications.
 

Continuously learn and adapt:

Stay curious, explore new techniques, and continue refining your skills to keep pace with advancements in AI technology.

Top Courses to Enter the Artificial Intelligence Field

Certification courses are a good way to stay and grow in AI Engineering. Leverage Careervira’s curated list of top-rated AI courses to build your career:

Computer Vision in Microsoft Azure by Coursera 

  • Description: This course will guide you on utilizing Computer Vision to analyze images, faces, texts, and receipts with Azure AI. 
     
  • Prerequisites: There are no prerequisites for this course.

Artificial Intelligence (Full Stack Data Scientist) Course in India by AI Patsala

  • Description: Industry experts designed this course for budding AI career seekers. It will teach you ML, Deep Learning, TensorFlow, and Artificial and Convolutional Neural Networks skills.
     
  • Prerequisites: You must have a strong command of programming languages, mathematics, complex algorithms, and basic knowledge of statistics and modeling.

Machine Learning Engineer for Microsoft Azure by Udacity 

  • Description: This course will improve your skills in building and deploying ML solutions using popular open-source tools and frameworks. It is specially designed for freshers and engineers. 
     
  • Prerequisites: You must have prior experience with Python, ML, and Statistics.

Google Cloud Certified Professional ML Engineer by Whizlabs 

  • Description: This course is designed for professionals who want to become Google Certified Professional ML Engineers. It prepares you for the Google Cloud Certified Professional ML Engineer exam.
      
  • Prerequisites: You must have a basic understanding of cloud concepts and coding in Python.

Become a Robotics Software Engineer by Udacity

  • Description: Those interested in starting their career as a robotics software engineer can pursue this course which teaches how to program robots using C++ and the ROS framework.
     
  • Prerequisites: You must have advanced knowledge of any OOP language, preferably C++. You should also have intermediate-level knowledge of probability, calculus and linear algebra. knowledge of basic Linux command lines is also required.

Data Scientist Nanodegree Program by Udacity

  • Description: This nanodegree program will teach you to set up data pipelines, create recommendation systems, run experiments, deploy cloud solutions, and manage them. 
     
  • Prerequisites: You must be well-versed in ML concepts, Python programming, probability, and statistics.

Introduction to Natural Language Processing with SpaCy by O’Reilly 

  • Description: This course is designed for intermediate Python programmers. It will teach you the basics of SpaCy and how it can be used for common tasks in NLP. 
     
  • Prerequisites: Prior knowledge of Python programming language.

Certification courses are a good way to stay and grow in AI Engineering. Leverage Careervira’s curated list of top-rated AI courses to build your career:

Computer Vision in Microsoft Azure by Coursera 

  • Description: This course will guide you on utilizing Computer Vision to analyze images, faces, texts, and receipts with Azure AI. 
     
  • Prerequisites: There are no prerequisites for this course.

Artificial Intelligence (Full Stack Data Scientist) Course in India by AI Patsala

  • Description: Industry experts designed this course for budding AI career seekers. It will teach you ML, Deep Learning, TensorFlow, and Artificial and Convolutional Neural Networks skills.
     
  • Prerequisites: You must have a strong command of programming languages, mathematics, complex algorithms, and basic knowledge of statistics and modeling.

Machine Learning Engineer for Microsoft Azure by Udacity 

  • Description: This course will improve your skills in building and deploying ML solutions using popular open-source tools and frameworks. It is specially designed for freshers and engineers. 
     
  • Prerequisites: You must have prior experience with Python, ML, and Statistics.

Google Cloud Certified Professional ML Engineer by Whizlabs 

  • Description: This course is designed for professionals who want to become Google Certified Professional ML Engineers. It prepares you for the Google Cloud Certified Professional ML Engineer exam.
      
  • Prerequisites: You must have a basic understanding of cloud concepts and coding in Python.

Become a Robotics Software Engineer by Udacity

  • Description: Those interested in starting their career as a robotics software engineer can pursue this course which teaches how to program robots using C++ and the ROS framework.
     
  • Prerequisites: You must have advanced knowledge of any OOP language, preferably C++. You should also have intermediate-level knowledge of probability, calculus and linear algebra. knowledge of basic Linux command lines is also required.

Data Scientist Nanodegree Program by Udacity

  • Description: This nanodegree program will teach you to set up data pipelines, create recommendation systems, run experiments, deploy cloud solutions, and manage them. 
     
  • Prerequisites: You must be well-versed in ML concepts, Python programming, probability, and statistics.

Introduction to Natural Language Processing with SpaCy by O’Reilly 

  • Description: This course is designed for intermediate Python programmers. It will teach you the basics of SpaCy and how it can be used for common tasks in NLP. 
     
  • Prerequisites: Prior knowledge of Python programming language.

Artificial Intelligence (AI) Jobs with Salaries (US, UK & India)

AI Engineering has continued to grow in importance in 2024 as organizations across various industries recognize the potential of artificial intelligence (AI) to drive innovation, improve efficiency, and create new opportunities. It is a growing sector with lucrative job opportunities.

The average annual salary of an Artificial Intelligence (AI) Specialist in India is ₹5,868,126; in the US, it is $70,488; and in the UK, it is £57,800.

AI Engineering has continued to grow in importance in 2024 as organizations across various industries recognize the potential of artificial intelligence (AI) to drive innovation, improve efficiency, and create new opportunities. It is a growing sector with lucrative job opportunities.

The average annual salary of an Artificial Intelligence (AI) Specialist in India is ₹5,868,126; in the US, it is $70,488; and in the UK, it is £57,800.

Final Thoughts

AI is moving forward and taking over traditional computing methods, changing how many industries perform daily operations. The incrementing data and need for data analytics open up wider opportunities to build your artificial intelligence engineer career. All you need is guidance to ensure success in the field. 

According to the Times of India, the AI industry will reach a valuation of $1,581.70 billion by 2030, with a CAGR of 38.0% by 2030. Considering this data, opting for a career in AI seems a great choice and the best time to prepare for it is now!

AI is moving forward and taking over traditional computing methods, changing how many industries perform daily operations. The incrementing data and need for data analytics open up wider opportunities to build your artificial intelligence engineer career. All you need is guidance to ensure success in the field. 

According to the Times of India, the AI industry will reach a valuation of $1,581.70 billion by 2030, with a CAGR of 38.0% by 2030. Considering this data, opting for a career in AI seems a great choice and the best time to prepare for it is now!

Features

Table of Contents

  • What is Artificial Intelligence (AI) Engineering?

  • What Does an Artificial Intelligence Engineer Do?

  • How to Become an Artificial Intelligence (AI) Engineer in 2024?

  • Top Courses to Enter the Artificial Intelligence Field

  • Artificial Intelligence (AI) Jobs with Salaries (US, UK & India)

  • Final Thoughts