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Programming

In 2021, Here’s How To Get A Job In Artificial Intelligence

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Machine learning engineers and data scientists receive far more than DevOps engineers, software engineers, and web developers on an annual basis.

Our future is already being shaped by artificial intelligence (AI) and machine learning, and the market for skilled engineers is on the rise. The machine learning market is expected to be worth nearly $31 billion by 2024, according to Market Research Future.

Nitin Gupta, India Today’s technology head for digital technologies and a Great Learning mentor and alumni, talked about artificial intelligence as a profession at SkillUp 2021.

Gupta has worked with companies including Lenskart and Senior World, and has 14 years of experience in interaction and implementation, technical programme management, and agile software development. He’s also a co-founder of Zercross, a smartphone and web app company.

AI-related applications
Every business has been affected by artificial intelligence. According to Gupta, the majority of AI innovation occurs in the research and development laboratories of large tech firms such as Amazon, Apple, Facebook, Google, and Microsoft.

Artificial intelligence has applications in a variety of fields, including:

  • Healthcare
  • Business
  • Education
  • Autonomous vehicles
  • Social media
  • Travel and tourism
  • Create a better world

Later, Gupta discussed a few artificial intelligence subsets, such as natural language processing (NLP), computer vision, and so on.

A typical organization’s life cycle for an AI project was also discussed:

Data engineering entails preparing and transforming data into formats that can be used by other team members.
Modeling: Searches for trends in data that can assist a business in predicting the results of different decisions, identifying business risks and opportunities, and determining cause-and-effect relationships.

Deployment: Takes a stream of data and integrates it with a specification, then tests the integration before deploying the model.


Market analysis: Evaluates the efficiency and business value of a deployed model and make adjustments to maximize profit or eliminate ineffective models.

AI infrastructure: Develops and maintains software systems that are dependable, fast, stable, and scalable to assist people working in data engineering, modeling, implementation, and business analysis.

Gupta identified various job positions in the field based on these tasks, including data scientists, machine learning engineer, data analysts, software engineer – ML, machine learning researcher, and software engineer.

“While there are some overlaps in the types of tasks that each of these individual positions manages, the scope of the skills needed on each task complements one another.”

Gupta explained the distinction between machine learning engineers and data scientists by stating that ML engineers need more hands-on data engineering, modelling, implementation, and AI infrastructure than data scientists. The data scientist, on the other hand, is responsible for activities such as data engineering, modelling, and market analysis.

Machine learning engineers must have a background in software engineering, algorithmic scripting, and machine learning skills. Data scientists, on the other hand, must have prior experience with machine learning, mathematics, data science, and business acumen.

Salary Trend
“AI and ML work salaries are higher than other job profiles, such as full-stack, back-end, and front-end developers, or native engineers in Android or iOS. However, pay scales are determined by a variety of variables, including education, interview process, expertise, and experience, among others,” Gupta explained.

According to an Indeed survey, from 2016 to 2018, the demand for artificial intelligence jobs increased multifold year over year, according to Gupta (YoY).

Machine learning engineers and data scientists receive significantly more than DevOps engineers, software engineers, and web developers, according to the study. He also mentioned that machine learning and data scientists are among the top 20 new occupations worldwide.

In India, the top three cities for AI/ML employment are Bengaluru, Delhi NCR, and Mumbai, followed by Hyderabad, Pune, and Chennai.

“Always be curious because it will benefit you in the long run,” Gupta said, adding that AI will continue to be at the center of technological progress and market growth in the future.


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