Table of Contents

Machine Learning Engineer Salary

In 2024, being a Machine Learning Engineer is still a top-paying job in the tech world. These experts are in high demand because they create smart computer systems that help businesses and technology advance. So, let’s explore the salaries of Machine Learning Engineers in 2024 and see how well they get paid for their skills and knowledge.

Who is a Machine Learning Engineer

Machine Learning (ML) is a part of Artificial Intelligence (AI) that involves using data analysis and math to make computers act smart like humans. It’s all about creating computer programs that learn from data patterns to make predictions. Think of it like how people get better at things with practice. The more data it has, the more accurate its predictions become, just like how humans get better at something with practice.

Responsibilities Of Machine Learning Engineer

ML engineers do important work. They take raw data, make it into useful models, and work with data scientists. They also create programs that help machines, computers, and robots process data and find patterns. Their job is to make sure machine learning works well for different tasks.

Machine Learning Engineers and Data Scientists both deal with large volumes of data, requiring strong data-handling skills. However, their roles differ in some key ways.

Typically, Machine Learning Engineers collaborate with Data Scientists. Data Scientists extract valuable insights from extensive datasets and share this information with business partners. Meanwhile, Machine Learning Engineers ensure that the models used by Data Scientists can process vast amounts of real-time data to generate more accurate results.

Machine Learning Engineer Salary in India

On average, a machine learning engineer earns an annual total compensation ranging from 7.5 to 8 lakh. The national average is approximately 7.6 lakh, while Payscale reports it as 7 lakh. These figures are derived from around 550 different profiles.

Machine Learning Engineer Salary Deciding Factors

1. Experience

The total years of experience in the relevant domain play a significant role in your ability to understand problems and provide effective, production-ready solutions. It is also a key determinant in your total compensation package.

2.  Location:

In the current remote working environment, the impact of location on compensation is less significant. However, there may still be slight variations in compensation based on factors such as the cost of living in different areas and the presence of industry hubs.

3. Company:

The choice of company is indeed a crucial factor in determining your total compensation. It holds equal importance to your level of experience and directly influences your salary and benefits package. Different companies have varying compensation structures and policies, which can significantly impact your overall remuneration.

4. Skillset:

Your skillset is like the key to unlocking opportunities. Having the right skills is essential for acing interviews and excelling in your role at a company. This, in turn, can lead to long-term growth in your compensation and career prospects.

Skills Required For Machine Learning Engineer

Skills and knowledge in the following areas can greatly benefit a machine learning engineer’s career:

1. Cloud-Based Container Ecosystems: Proficiency in container technologies such as Docker, Mesos, and Kubernetes is valuable for deploying and managing machine learning models and applications efficiently in cloud environments.
2. Natural Language Processing (NLP): An understanding of NLP techniques and the use of deep neural networks like RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and CNN (Convolutional Neural Networks) is important for tasks involving text analysis, sentiment analysis, and language processing.
3. GPU and Cuda/CuDNN: Knowledge of working with GPUs (Graphics Processing Units) and frameworks like Cuda/CuDNN is beneficial for accelerating machine learning model training and inference, especially for computationally intensive tasks.
4. Profiling and Low-Level Optimizations: Proficiency in profiling tools and techniques helps identify bottlenecks in machine learning workflows, allowing for low-level optimizations to improve performance and efficiency.

These skills, when combined with a strong foundation in machine learning, can make a machine learning engineer a valuable asset in developing and deploying machine learning solutions efficiently and effectively.

FAQ – Machine Learning Engineer Salary 2024

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Electric Engineer Salary

Electrical engineers are important in India as they help with technology and power. They work in various areas like making electronics and communication systems. To know how much they earn, we look at their salaries. The money they make depends on how long they’ve been working, where they work, what they’ve learned, and what they specialize in. In this article, we will learn about how much electrical engineers in India usually get paid at different stages of their careers and in different parts of the country. This will help us understand how much they earn in India.

Who is the Electric Engineer

Electrical engineers are experts who work with various components like batteries, capacitors, conductors, coils, diodes, inductors, magnets, resistors, switches, and transistors to create electronic designs. Their job requires a deep understanding of electronic and electrical theories, materials, and mathematics. They design circuits that perform specific functions while ensuring safety, reliability, and energy efficiency.

In their work, electrical engineers increasingly rely on computer-aided design (CAD) systems to create schematics and circuit layouts. They also use computer simulations to virtually test how devices and systems will operate. Computer technology helps them model circuits, processors, or entire grids, speeding up drafting processes, printing circuit board designs, and device blueprints. It also allows for design modifications and rapid prototyping.

Originally, electricity was primarily used for transmitting information and energy. While these aspects remain important, electrical engineering has branched into specialized sub-disciplines. Electrical engineers can choose to focus on one area or work across multiple disciplines.

Job Responsibilities Of Electric Engineer

Your description outlines several key responsibilities and tasks commonly associated with electrical engineers.

1. Design Innovative Electrical Systems: Electrical engineers are responsible for coming up with new ways to utilize electrical power to either create new products or improve existing ones. This includes designing circuits, systems, and devices to meet specific requirements and objectives.
2. Precise Calculations: Detailed calculations are a crucial aspect of an electrical engineer’s job. These calculations are used to develop manufacturing, construction, and installation standards and specifications. This ensures that electrical systems are designed and built to meet safety, performance, and regulatory standards.
3. Project Management: Electrical engineers often play a significant role in project management. They oversee the manufacturing, installation, and testing of electrical equipment to ensure that products meet specifications, safety codes, and quality standards. This involves coordinating with various teams and stakeholders to ensure the successful completion of projects.
4. Problem Solving: Investigating complaints and issues from customers or the public is a common task. Electrical engineers evaluate problems, conduct root-cause analyses, and recommend solutions to address electrical system malfunctions or deficiencies.
5. Collaboration with Project Managers: Collaborating with project managers is essential for ensuring that electrical engineering projects are executed efficiently. This includes working together to define project goals, budgets, timelines, and resource requirements to ensure projects are completed on time and within budget.

Overall, electrical engineers play a critical role in designing, implementing, and maintaining electrical systems that power various applications and products, from consumer electronics to industrial machinery. Their work requires a strong blend of technical expertise, problem-solving skills, and project management abilities to ensure the success of electrical engineering projects.

How To Become An Electric Engineer

Becoming an electrical engineer typically involves pursuing higher education, but there are various paths to enter the profession. You can choose to follow a university degree course, a college program, an apprenticeship, or work your way up from a related discipline.

If you opt for the university route, you will enroll in a degree program related to electrical engineering. These programs may go by different names, such as electrical or electronics engineering, electrical engineering technology, or electrical and electronic engineering. Regardless of the specific program, they all teach fundamental principles of electrical engineering. After completing your degree, you will hold a Bachelor’s degree, which can vary in title depending on the university and program, such as Bachelor of Science in Electrical/Electronics Engineering Technology, Bachelor of Engineering, Bachelor of Science, Bachelor of Technology, or Bachelor of Applied Science.

During your bachelor’s degree, you’ll study a wide range of subjects, including computer science, physics, mathematics, and project management, in addition to electrical engineering topics. You may also have the opportunity to specialize in specific sub-disciplines of electrical engineering.

After obtaining your bachelor’s degree, you can choose to pursue further education at the postgraduate level. This may involve earning a Master of Engineering (MEng) or Master of Science (MSc) degree, a Master of Engineering Management, a Doctor of Philosophy (PhD) in Engineering, an Engineering Doctorate (EngD), or an Engineer’s degree. These advanced degrees offer specialization and can enhance your expertise and career prospects in the field of electrical engineering.

Types Of Electric Engineer

1. Control Engineering

This discipline is centered around control systems, which are designed to ensure that various systems operate according to specific desired behaviors. These systems encompass a wide range of applications, from aerospace flight and propulsion systems to industrial automation and robotics. Control systems are typically created to offer feedback on performance, enabling the monitoring of potential issues or enabling automated responses in autonomous devices like drones, vehicles, and other systems.

2. Computer Engineering

Computer engineers specialize in designing computers and computer systems, including the hardware components found in devices like desktop and laptop computers, video game consoles, mobile phones, robots, and more. Although software engineering is typically regarded as a distinct field, computer engineers may also be involved in developing a system’s software alongside their hardware-related tasks.

3. Electronics Engineering

Originally referred to as radio engineering, this branch of engineering primarily dealt with the creation and evaluation of electronic circuits used in radios, communication systems, radar systems, and the early stages of television technology. Over time, the scope of this field expanded to encompass contemporary television and audio systems as well as the development of computers and microprocessors.

4. Instrumentation Engineering

This area of engineering is focused on the design of devices used to measure physical quantities like flow, pressure, and temperature. It involves applying principles from physics to create instruments used in various applications, including aviation, automotive technology, and industrial processes. For example, engineers in this field develop instruments such as thermocouples, which play a crucial role in maintaining a consistent temperature in industrial furnaces and other equipment.

5. Micro and Nanoelectronic Engineering

Microelectronics deals with tiny electronic components at the microscopic level, while nanoelectronics takes it a step further, working with even smaller components at the nanometer scale.

There are also several other types of electric engineering which include Photonic and Optic Engineering, Power and Energy Engineering, Signal Process Engineering, and Signal Process Engineering

Electric Engineer Salary

Experience is the primary factor influencing salary, with more years of experience generally leading to higher wages.

Electric Engineer with Less than two years of experience will earn Around 17,300 INR. Whereas, the one with Two to five years of experience will be earning Approximately 24,600 INR, which is 42% higher than those with less than two years of experience. Then, Engineers with Five to ten years of experience will earn about 32,300 INR, representing a 31% increase compared to those with two to five years of experience. Engineers with Ten to fifteen years of experience can expect a salary of approximately 39,700 INR, which is 23% more than those with five to ten years of experience. Fifteen to twenty years of experience will earn around 42,200 INR, a 6% increase compared to those with ten to fifteen years of experience. Additionally, engineers with more than twenty years of experience can approximately earn 46,300 INR, a 10% increase compared to those with fifteen to twenty years of experience.

Skills Required For Electric Engineer

• Critical thinking
• Technological knowledge
• Active learning
• Interpersonal skills
• Organizational skills
• Complex problem-solving skills
• Mathematics and physics skills
• Innovative thinking

FAQ- Electric Engineer Salary In India 2023

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

Table of Contents

Data Science Vs Machine Learning

This article will help us understand the differences between Data Science and Machine Learning, and how they work together to shape our tech-driven world. Although people often use these terms interchangeably, they have some key differences.

Data Science covers everything from gathering data to analyzing it to finding useful insights. On the other hand, Machine Learning is a part of artificial intelligence that focuses on creating algorithms to help machines learn from data and get better at tasks over time.

What is Data Science?

Data Science is considered a broad, multidisciplinary field that values from today’s massive data sets. Even though, it has advanced tools that will monitor raw data, collect a data set, and create insights to create meaning. The various fields of Data Science are Mining, Statistics,  data analytics, data modeling, machine learning modeling, and programming.

Data Science will help businesses find out new problems. Once these problems are identified, Machine Learning and statistical analysis will look into them to find solutions. Data Science will monitor the business issue, figure out what data is needed, and then analyze that data to solve the real-world problem.

What is Machine Learning?

Machine learning (ML) is a sub-division of artificial intelligence (AI) that looks into learning from data science. It needs data science tools for the initial cleaning, preparation, and monitoring of the unstructured big data. Moreover, Machine learning can learn from the data to create insights that can improve performance or inform predictions.

In machine learning, machines use tools to analyze data. They work on specific problems using various techniques and tools, creating algorithms that allow them to learn from data through experience with very little help from humans. Moreover, they can handle huge amounts of data, and they get better as they process more data.

Difference Between Machine Learning and Data Science

Conclusion

In conclusion, while data science and machine learning are closely related, they serve distinct purposes. Data science involves extracting insights and knowledge from diverse datasets, encompassing various techniques. On the other hand, machine learning is a sub-division of data science, focusing specifically on creating models that can learn and make predictions.

Both are valuable tools, with data science providing a broader analytical framework, and machine learning offering specific algorithms for automated learning. The choice between them depends on the goals and requirements of a particular task or project.

Data Science Vs Machine Learning-FAQ

Hridhya Manoj

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

When it comes to rolling the dice, 6 is the real 10.

As 2022 hits the pedal, we all are looking forward and making plans for the year—but sometimes to move ahead, you must look back. 

 How did 2022 lay the base for 2023?

I think it goes without saying that 2022 was a year of transformation. Even though 2021 was about survival, 2022 was the year when people, corporations, and society started looking ahead to influence their futures.

Last year resulted in a K-type recovery with companies that had invested in technology, digital, and data. 

Note– A K-shaped recovery is an economic recovery following a recession where only certain sectors, industries, or areas of the economy recover while others persistently lag.

While it is difficult to make accurate predictions in an unpredictable environment, it is without a doubt that the year ahead will bring in several golden opportunities. 

And, we want you to have all the arrows in your quiver!

What are the top 6 online upskilling programs to start with?

As more businesses in India embrace digital ways of working, career opportunities in high-growth fields are on the rise, and employers are seeking to bridge the gap for trained talent.

It is your time to shine! 

Check out the following areas in the world of technology.

Just a side note, although these fields can be pursued individually, I suggest you, at least, check out the beginner courses for all of the following to build a strong and versatile foundation.

After all, a jack of all trades is better than a master of one.

1- Machine Learning and Artificial Intelligence

Did you know that Eugene Goostman, a chatbot, managed to pass the Turing Test?

Alan Turin created the test in 1950 to evaluate a machine’s intelligence. If the machine could convince a human that they were actually talking to another human, the machine would pass the test. Eugen managed to achieve this in 2014, making 1 out of 3 judges think that it was human.

No! For the last time, machines are not going to take over humans. The fact above just goes to show the ever-lasting potential of Machine Learning and Artificial Intelligence.

Leaders predict that Ethical AI frameworks will play a significant role in 2022. Considering how Big Tech has been held accountable for its biased AI algorithms, we will see an increased focus on the responsible and ethical development of AI/ML. 

Along with ML decision-making processes, several ethical tests will also be extended to data privacy. Companies will start standardizing their processes accordingly, and we will see a stark change in the organization’s data strategy.

In short, India will need more ML/AI experts and you can be one of them.

Check out the Skillvertex Machine Learning and Artificial Intelligence Upskilling programs and let us help you transform your curiosity into expertise.

2- CyberSecurity

What do you call a rehabilitation meeting for alcoholic hackers? Anonymous Anonymous.

The above is a joke (lame? probably). 

However, the threat of breaches and leaks in information frameworks is extremely real. 

In recent years, there is a significant increase in the number of cyberattacks targeting software supply chains. These attacks are particularly effective because they can take down an organization’s entire software supply chain and services, resulting in massive business disruptions. 

Unfortunately, we can expect these attacks to become even more common in 2022. 

Cybercriminals will realize that these supply chain attacks are an effective way to cause maximum disruption, and once inside the trusted gates, the hardest part of the hack job is already done.

Cyber Security professionals are responsible for preventing such attacks and protecting the company. There are more than 3,000 jobs available for professionals in Cyber Security in top MNCs.

Join the Skillvertex Cyber Security Upskilling Program and put your chit in the hat. This just might be the lottery you are destined to win.

3- Data Science

Without data science, companies would be blind and deaf, wandering out onto the web like a clueless deer on a freeway.

Data Science, an interdisciplinary field, is a mixture of multiple Machine Learning algorithms, tools, and techniques that aim to extract trends and patterns from raw and unstructured business data. It is not a new concept for businesses any more. It has become an integral cog in the wheel of business, especially for enterprises that rely on data to gather insights. 

Did you know Netflix was able to influence 80% of its content just by analyzing data from its 100+ million subscribers?

Data Science professionals have an expert understanding of several mathematical and statistical techniques using which they draw conclusions to the problems and help in the growth and development of the organization.

There are over 18,000 Data Science jobs available for both entry-level and experienced professionals in India.

Trust me. It does not get better than this.

Check out the Skillvertex Data Science Upskilling Program and answer your calling.

4- Full Stack Web Development

Every coin has two sides- heads or tails. 

If you cannot choose, just grab the coin.

No. I am not asking you to steal. I am just asking you to get the best of both worlds.

Web development is divided into two parts- the aesthetics of the front end and the technology of the back end. However, to become the master of both, you would have to become a full-stack developer.

Full-stack web development is one of the most demanded and versatile fields. As a full-stack developer, you would be able to handle servers, databases, as well as clients. There are different kinds of stacks that one needs to use based on the project’s requirements. 

Currently, India has over 19,000 jobs available for Web Developers in top MNCs. 

The demand for full-stack developers will steadily increase in the market as new technologies enter. While it comes with its bells and whistles, you will have to learn a lot of stuff that other developers do not have to, and it can seem a little daunting to someone.

Don’t worry!

Dot the i’s and cross the t’s with the Skillvertex Full Stack Web Development Upskilling Program where not only you will learn the skills you need, but also get the right career guidance.

5- Digital Marketing

They say the best place to hide evidence is Page 2 of Google Search.

However, if you are a company in search of an online presence, you would want to be on the first page.

Digital Marketing is the best way of marketing and advertising numerous products and services of various companies. With the help of the plethora of digital platforms available, such as websites, mobile applications, email, social media, search engine rankings, and more, Digital Marketers can advertise whatever their companies offer. 

Digital Marketing is a broad field that includes Email Marketing, Content Writing, Social Media Marketing (SMM), Search Engine Optimization (SEO), etc. However, the main responsibility of a Digital Marketer, irrespective of the department they work in, is to create broad awareness of their brand and generate leads through free and paid digital channels. 

With more and more organizations shifting to make an online presence and expand their clientele, the demand for Digital Marketing has reached new heights.

There are over 14,000 jobs available for Digital Marketing professionals specializing in the numerous sectors of this domain.

And, this seems like a good place for you to start.

Join the Skillvertex Digital Marketing Upskilling Program and take that first step towards a glowing career.

6- Finance

Money is a terrible master but an excellent servant.

We have seen trends come and go; the only thing that remains constant is money.

If you find finance intimidating- welcome to my world. All of us think that finance is all about mavericks dressed in black panicking over frantic stock tickers. However, from what I have researched, finance has a seat at every table.

It does not matter if you work in tech or education, consulting or advertising. Your company needs cash flow to pay salaries, distribute dividends, and reinvest in product innovation.

Finance is at the core of business decision-making. If you’re pitching a project or angling for additional funding, understanding and being able to communicate how you can turn that investment into revenue for your organization will help you make a more convincing argument.

If you’re entering the workforce as a young professional without extensive experience, showing you have business knowledge makes you a more attractive candidate. This is particularly relevant in non-finance fields, where financial education is less common but no less fundamental to running a successful business.

The world of finance has grown beyond traditional big banks. The need is everywhere. Learning how to take quantitative data and use it to solve problems and make sound business decisions is a valuable skill that can serve you in your career now and in the future.

So, join the Skillvertex Finance Upskilling Program and become a real-world problem solver.

Artificial Intelligence and Machine Learning: What’s the difference?

Artificial Intelligence (AI) and Machine Learning (ML) are two of the most talked-about and rapidly evolving fields in technology today. AI refers to the ability of machines to perform tasks that typically require human intelligence, such as natural language processing, decision-making, and problem-solving.

On the other hand, ML is a subset of AI that involves the use of algorithms to enable machines to learn from data, identify patterns, and make decisions without explicit instructions. In this blog post, we’ll take a closer look at the differences between AI and ML, how they are related, and how they are transforming various industries.

Artificial Intelligence:

AI, or Artificial Intelligence, refers to the ability of machines or computer programs to perform tasks that would typically require human intelligence, such as reasoning, problem-solving, decision-making, understanding natural language, and recognizing patterns. 

AI involves the development of algorithms, models, and systems that can simulate human cognition and behavior, allowing machines to perform complex tasks more efficiently and effectively. 

AI has various applications in fields such as healthcare, finance, education, transportation, and many others, and is transforming the way we live and work.

Machine Learning:

ML, or Machine Learning, is a subset of AI that focuses on the development of algorithms and statistical models that allow machines to learn from data, identify patterns, and make decisions without being explicitly programmed. 

In other words, ML involves building computer programs that can automatically improve their performance on a specific task by learning from experience, without being explicitly programmed to do so. ML algorithms are designed to automatically identify patterns in data, learn from those patterns, and make predictions or decisions based on that learning. 

ML has many practical applications, such as image and speech recognition, natural language processing, recommendation systems, fraud detection, and predictive maintenance, among others.

Difference Between AI and ML:

AI and ML are two of the most exciting fields in technology today, and are often used interchangeably. However, they are not the same thing. AI refers to the capability of machines to perform tasks that usually require human intelligence, such as decision-making, problem-solving, and natural language processing. 

Think of AI as the parent category, and ML as one of its children. While both are related, they have some fundamental differences. AI focuses on creating intelligent machines that can think and work like humans, whereas ML focuses on teaching machines to learn from data and improve their performance over time.

Another key difference is their level of autonomy. AI systems can operate independently and make decisions on their own, while ML algorithms require input and guidance from humans to improve their performance.

Overall, AI and ML are transforming the way we live and work, and their applications are increasingly present in our daily lives. From chatbots to self-driving cars, from voice assistants to personalized recommendations, AI and ML are making our lives easier, more efficient, and more exciting. So buckle up, and let’s explore these fascinating fields together!

The key differences between AI and ML:

AI (Artificial Intelligence) and ML (Machine Learning) are two related but distinct concepts. AI refers to the ability of machines to perform tasks that typically require human intelligence, such as decision-making, problem-solving, and natural language processing. 

ML, on the other hand, is a subset of AI that focuses on developing algorithms that enable machines to learn from data, identify patterns, and make decisions without explicit instructions.

Take your  ML skills to the next level and enroll in the SkillVertex Machine Learning Course 

• Learn everything you need to know about creating automated learning systems and understanding ML algorithms with the Skillvertex Machine Learning Course.
• Explore the various applications of AI and learn how to create dashboards, storytelling, and deploy models.
• Intern with Skillvertex on live projects and develop a strong skill set for personal and professional growth.
• Gain insight into the exciting field of AI and become familiar with which algorithms and models to implement for different problem statements.

Application differences between AI and ML:

Let’s explore this fascinating world of AI and machine learning together.

In this table, we’ll take a closer look at how these two cutting-edge technologies are used in various applications. From robots that can learn from experience to virtual assistants that can understand human language, the table will explore the exciting differences in how AI and ML are changing the way we work, play, and live. 

So, buckle up and get ready for an informative and entertaining journey through the exciting applications of AI and ML!

Take your AI skills to the next level and enroll in the Skillvertex Artificial Intelligence Course

What you’ll learn in the Skillvertex Artificial Intelligence course

• Introduction to Artificial Intelligence and Machine Learning
• Programming automated learning systems using Python
• Understanding various ML algorithms and their use in setting up a learning environment
• Learning about the various applications of AI
• Creating dashboards, storytelling, and deploying models
• Interning on live projects to gain practical experience
• Developing AI skills for personal and career growth
• Gaining insights into Artificial Intelligence
• Knowing which algorithm/model to implement for different problem statements

The World Economic Forum states that AI and ML specialists are one of the most emerging jobs in India. There has been a threefold increase in the demand for Machine Learning specialists in India as well. Owing to this, companies in India are not shying away from increasing machine-learning expert salary packages. 

It is said that if we imagine machines as a baby, currently, in terms of ML, we stand where the machines are just learning how to crawl. However, soon, the machines will not just walk but fly.

But before we get into machine-learning expert salary packages, let us look at what ML is all about in India.

What is Machine Learning?

Machine Learning is a method that uses data analysis to automate analytical model building. This branch of artificial intelligence is based on the idea that systems can learn from data. They can identify patterns and make decisions with minimal human intervention. 

Although machine-learning expert salary packages are getting hiked today, ML is not a new concept. In fact, during World War II, Alan Turing developed the Enigma machine to break Germany’s code. Machine Learning is everywhere today. This has caused an increase in machine-learning expert salary packages.

Why is Machine Learning important in India today?

Before we see what has caused the increase in machine-learning expert salary packages in India, we need to understand the importance of ML in India.

The most important aim of ML is to enhance the overall productivity, functioning, and decision-making processes of organizations. As machines begin to learn through algorithms, businesses will unravel patterns within data. They offer high machine-learning expert salary packages to ensure that pattern recognition and performance enhancement is done without human intervention. Apart from these, ML has the following benefits which have caused a hike in machine-learning expert salary packages-

1-Time Analysis and Assessment-

ML makes it easier for one to sift through large customer feedback and interaction databases. It helps organizations to conduct timely analysis and assessment of their strategies. When organizations hire specialists at high machine-learning expert salary packages, they create business models that browse through the data to see relevant variables.

2-Real-time Predictions through Fast Processing

ML algorithms are super fast which makes data processing from multiple sources easy. This makes real-time predictions easy for organizations causing a hike in machine-learning expert salary packages in India.

Churn Analysis: 

Another instance of reason for the rise in machine-learning expert salary packages in India is the ability to identify customer segments that are likely to drop your brand. This saves a lot of money for businesses.

Customer leads and conversion- Another way in which high machine-learning expert salary packages helps is that ML provides valuable insights into buying and spending patterns of customer segments. This enables businesses to minimize losses.

Customer Retention-  With the help of high machine-learning expert salary packages, ML specialists can improve the strategies to improve customer experience.

3-Transforming Industries

ML has started transforming industries with the ability to provide valuable real-time insights. Hence, it is obvious with increasing demand, there is a stark increase in machine-learning expert salary packages. Finance and insurance companies are leveraging ML to prevent fraud and provide customized financial plans to different customer segments by hiring at a high machine-learning expert salary package. 

ML is also entering the healthcare sector in the form of wearables and fitnesses packages. This has caused a hike in high machine-learning expert salary packages in the healthcare industry to enable individuals to take charge of their health. 

Owing to the above, there is a substantial need for ML engineers which, in turn, has caused an increase in machine-learning expert salary packages. However, there is one another major reason that has led to this spike in India.

What has caused an increase in the machine-learning expert salary package?

Aspiring Minds’  Annual Employability Survey states that 80% of engineers in India are not well-suited for our knowledge economy. Only 2.5% of engineers possess the skills to get high machine-learning expert salary packages.

So, we need to understand what machine-learning engineers do and why there is an increase in machine-learning expert salary packages.

What does a Machine Learning Engineer do?

A machine learning engineer is a job role similar to that of a data scientist. Both data scientists and ML specialists work with large data sets and need to possess excellent skill sets. Hence, both data scientists’ salaries and machine-learning experts’ salary packages are high.

However, ML engineers heavily focus on designing self-running software for predictive model automation. They ensure that the models used by data scientists extract meaningful insights leading to an increase in machine-learning expert salary.  

There are several other reasons that have led to the spike in machine-learning expert salary such as the additional responsibilities they have.

Machine-learning Expert Salary in India

The demand for ML is high but companies require candidates with the right skill sets. Hence, there is a demand to hike machine-learning expert salary packages.

The average machine-learning expert salary in India is Rs. 686,281 according to Payscale.  

However, your machine-learning expert salary package depends on your skills and experience.

The machine-learning expert salary package can go as low as Rs. 303,000. 

Similarly, the highest machine-learning expert salary is as high as Rs 2 million.

Factors Affecting Machine-Learning Expert Salary in India

1-Company

The bigger the brand, the higher the salary people can expect. Tata Consultancy Services Ltd. offers about Rs. 4,42,000 whereas Intel Corporations offers a machine-learning expert salary package of 20,00,000.

2-Experience

Entry-level machine-learning expert salary in India is Rs. 501,058.

Mid-level machine-learning expert salary in India is Rs. 1,142,459.

Experienced machine-learning expert salary in India is Rs. 1,999,619.

3-Location

Being the Silicon Valley of India, Bangalore offers the highest machine-learning expert salary packages that are 21% higher than the national average. Whereas, Chennai, another city competing to be the silicon valley of India, offers machine-learning expert salaries. 

4-Skillset 

Some skills get you a higher package such as-

1. Machine Learning- Rs 706,169
2. Python- Rs 612,686
3. Deep Learning- Rs 754,250
4. Natural Language Processing – Rs. 697,670
5. Computer Vision- Rs. 736,976

How to start your journey to becoming an ML expert?

The answer is here.

When to start? 

Now. Skillvertex is an e-learning platform established in March 2021. They provide 26+ budget-friendly upskilling programs under a diverse range of domains such as Computer Science, Civil, Mechanical, Electronic and Communication, and Management.

These programs are divided into 4  categories-Training, Placement Assurance, Cohort, and Advanced. 

Not only do their experienced industry experts provide the students with in-depth knowledge of their fields, but also the masters interact with them one-on-one to clear their doubts. They focus heavily on practical knowledge through real-time projects in industry-simulated environments.

Additionally, they provide career counseling, and personality development sessions along with globally accepted certifications. 

They give everything they have to get their students the dream job they seek and deserve.

Skillvertex has partnered with several reputed institutions such as SRM Institute of Science and Technology, and Vellore Institute of Technology (VIT), and established partnerships with renowned companies such as Obeya and Artifintel among many others. 

Skillvertex has onboarded 10,000+ active learners on their platform. They were also awarded the honor of being the Best Edtech Platform ‘21 by CE Worldwide.

With a strong core team of 10 members, and 350+ employees, Skillvertex is working day and night to reach every corner of India and change the face of digital education for the better.

So, what are you waiting for? Get lifetime access to our LMS portal, expert guidance, and real-time industry experience to master the state-of-the-art technology of machine learning.

Machine Learning is the last big invention humanity will ever need. Your time to shine is now.