Salary of a Machine Learning Engineer in India (2023) – for Both Entry-level and Experienced Professionals – IQCode

Machine Learning: A Brief Overview

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that utilizes data analytics and mathematical modeling to simulate human-like intelligence in computers. It involves the study of computer algorithms that learn by recognizing patterns in data for future predictions. The more data available, the more accurate the predictions, similar to human cognitive abilities.

According to the graph, the trend for machine learning started in early 2015 and skyrocketed in 2018. In this article, we will cover the basics of Machine Learning engineering, its functions, compensation, job profile, responsibilities, skills, and requirements in India, as well as other countries.

Table Of Contents

* What is a Machine Learning Engineer, and what do they do?
* Machine Learning Engineer Salary in India
* Understanding the Factors that Decide Machine Learning Engineer Salary

1. Experience
2. Location
3. Company
4. Skillset

* Why is Machine Learning important in today’s world?

1. Predictive Analysis
2. Anomaly Detection
3. Pattern Recognition
4. Classification

* Job Roles and Responsibilities of a Machine Learning Engineer
* Essential Skills for a Machine Learning Engineer
* Steps to Become a Machine Learning Engineer

1. Learn Programming Languages
2. Learn Mathematics for Machine Learning
3. Learn the basics of Machine Learning
4. Gain knowledge of Framework and Packages
5. Familiarize yourself with NLP and DL

* Machine Learning Engineer Salary in Other Countries
* How to Prepare for Machine Learning Interviews
* Summary
* FAQs

1. What is the average salary for a Machine Learning Engineer in India?
2. Is Machine Learning a high paying profession?
3. Is it hard to become a Machine Learning Engineer?
4. Who earns more: Data Scientist or Machine Learning Engineer?
5. Is Machine Learning a good career choice?

* Additional Resources

What is a Machine Learning Engineer and What Do They Do?

A Machine Learning Engineer processes and scales data science models created by data scientists. They build algorithms and programs that allow machines and computers to detect various patterns. Their work is similar to that of a Data Scientist, as both have to deal with large amounts of data. However, Machine Learning Engineers’ main focus is to ensure that the models used by Data Scientists can handle a significant amount of data for generating precise results. They collaborate closely with Data Scientists to achieve this.


A machine learning engineer in India earns an average total compensation of 7.5 to 8 lakh per year. Glassdoor and Payscale data sets, based on approximately 550 different profiles, report the nationwide average to be 7.6 lakh and 7 lakh, respectively.


As a Machine Learning Engineer, your salary depends on four main factors:


Having relevant experience is crucial in delivering efficient and effective production solutions. It also plays a significant role in determining compensation for the position of a Machine Learning Engineer.
Here is a resource that provides information about the salary of a Machine Learning Engineer based on years of experience:

Average Salaries of Machine Learning Engineers by Location in India


In the current scenario of remote work, location has a negligible effect on compensation for machine
learning engineers in India. However, salaries may vary slightly based on the cost of living and industry hubs.

Location Average Total Compensation (in lakhs)
Bangalore 8.7
Chennai 7.25
Delhi 7
Gurgaon 5.35
Hyderabad 6.8
Kolkata 6.4
Mumbai 6.25
Noida 6
Pune 6.15


The salaries of machine learning engineers in India vary slightly based on location. The table shows average total compensation in lakhs for different locations in India. Even though remote work is in place now, location can still play a role in compensation to a small extent.

Impact of Company on Total Compensation

In terms of total compensation, the choice of company is crucial and equally important as experience. It determines your salary and benefits. Below is the average total compensation offered by some top companies:

Company Average Total Compensation
TCS 5 lakhs
Accenture 7.75 lakhs
Cognizant 5.5 lakhs
Infosys 6.3 lakhs
Google 12.15 lakhs
Wipro 5.6 lakhs
Qualcomm 14.2 lakhs
Oracle 10.35 lakhs

Note: All figures are in lakhs (Indian Rupees) per annum.

Building the Right Skillset for Career Growth

Aspiring professionals need to build an appropriate skillset to excel in job interviews and succeed in their careers, leading to higher compensation in the long run. Here’s the average total compensation for professionals with essential skills:

Skills Average Total Compensation
Machine Learning $70,000
Deep Learning $75,000
Natural Language Processing $73,000
Computer Vision $72,500
Artificial Intelligence $80,000

Remember, building a specific skill set can help you unlock many opportunities in the technology industry.

Importance of Machine Learning in Today’s World

Machine Learning and Artificial Intelligence have become crucial for businesses today. They are not limited to the tech industry and offer endless opportunities to improve a company’s efficiency and dynamic interaction with vast volumes of data. Here are some of the wonders that Machine Learning can do:
– Automate complex and repetitive tasks.
– Enhance the accuracy of decision-making.
– Predict and prevent system failures.
– Personalize user experiences.
– Analyze and extract valuable insights from unstructured data.

In summary, Machine Learning brings significant benefits and advantages to businesses, making it a crucial investment for companies looking to stay ahead of the game.

Predict Values using Regression Algorithms

Regression algorithms help to identify the relationships between variables and predict values based on given data. They can be used to forecast future demand for products, estimate marketing and sales projections, or predict survey results.

Identifying Unusual Occurrences with Machine Learning

Machine learning (ML) can effectively identify potential threats and detect irregularities based on historical data. By analyzing deviations from expected standards and ranges, ML-powered algorithms can detect hardware glitches, typos, abnormal fluctuations in sales numbers, and suspicious activities such as fraudulent or illegal transactions. This makes ML an ideal solution for addressing these concerns, especially in industries like payments.

Finding Structure in Data with Clustering Algorithms

Clustering algorithms are commonly used in the first stages of machine learning to reveal hidden subsets within datasets. This technique is particularly useful for market and competitor analysis, providing insights that can inform pricing decisions and reveal customer preferences. By grouping everyday items into clusters, we can recommend products that align with a customer’s interests and past purchases.

Predict Categories

The categorization algorithm helps determine the appropriate classification for data. It is particularly useful in the e-commerce industry, where numerous new products are added daily and must be accurately categorized to improve recommendations. Manually categorizing thousands of products each day is a cumbersome and error-prone task.

// sample code for categorization algorithm
function categorizeData(data) {
// implement algorithm here
return category;

Job Roles and Responsibilities of a Machine Learning Engineer

A Machine Learning Engineer is responsible for:

  1. Identifying, analyzing, clustering, and processing data to create data models
  2. Validating mathematical conditions and optimizing data model performance based on previous test results
  3. Developing neural network models that cater to business and customer needs
  4. Designing and implementing cutting-edge deep learning models for production use or to contribute to ML frameworks and libraries
  5. Having a deep understanding of data mining and algorithms
  6. Applying machine learning techniques to solve real-world problems

//sample code for implementing neural network models
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

//train the model with appropriate data

In this code snippet, we import the required libraries, define our neural network model, and train it on appropriate data.

Essential Skills for a Machine Learning Engineer

To become a machine learning engineer, one must be proficient in modern
programming languages such as Python, R, Scala, C++, Java, or Matlab.
Additionally, expertise in frameworks like Keras, Tensorflow, and PyTorch
is crucial, along with data analysis packages like NumPy, pandas, and

A machine learning engineer should have experience in data extraction,
processing, and storage using RDBMS and NoSQL databases, as well as tools
like Hadoop, Spark, or Hive. Below are some further skills that can help
you thrive in this role:

* Familiarity with cloud-based container ecosystems like Docker, Mesos,
and Kubernetes.

* Basic knowledge of Natural Language Processing and deep neural networks
such as RNN, LSTM, GRU, and CNN.

* Working knowledge of GPU, Cuda/CuDNN, profiling, and low-level

The image below summarizes the essential techniques and algorithms used by
machine learning engineers, and theoretical and practical proficiency in
these areas is critical.

Steps to Become a Machine Learning Engineer

To become a successful machine learning engineer, follow these steps:

1. Acquire a strong foundation in mathematics, statistics, and computer science.
2. Learn programming languages such as Python and R.
3. Familiarize yourself with machine learning algorithms and techniques.
4. Get hands-on experience with machine learning tools and frameworks.
5. Practice implementing machine learning models on real data problems.
6. Stay up to date with the latest developments in the field.

Remember, relevant practice at every step is crucial to achieving excellence.

Learning Python for Machine Learning

Python is a widely used programming language for machine learning tasks because of its simplicity, community support, and abundance of resources, such as packages and frameworks. It is recommended to start with the fundamentals of programming before moving on to advanced concepts.

Once familiar with Python basics, you can focus on learning the packages specific to machine learning, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, and Plotly.

Math Fundamentals for Machine Learning

Mathematics is the cornerstone of machine learning algorithms. It’s crucial to grasp both the basics and advanced concepts, including statistics, probability, linear algebra, derivatives, and partial derivatives, to achieve proficiency.

Essential Machine Learning Basics

To build a strong understanding of machine learning, you must first establish a solid foundation. Below are some critical topics to learn:

1. Linear Regression

2. Cross-Validation and Bias-Variance Trade-off

3. Logistic Regression

4. K-Nearest Neighbors (KNN)

5. Decision Trees and Random Forests

6. Support Vector Machines (SVM)

7. K-Means Clustering

8. Principal Component Analysis (PCA)

9. Recommendation Systems

These fundamental concepts will pave the way for more advanced machine learning techniques.

Frameworks and Packages:

There are several open-source frameworks available that simplify the implementation of algorithms and concepts mentioned above. It is crucial to learn these frameworks and utilize their libraries in our preferred programming language. Popular frameworks with excellent community support include TensorFlow, Keras, Torch, and PyTorch.

Natural Language Processing (NLP) and Deep Learning (DL)

To become a strong machine learning engineer with a solid foundation, follow the steps above. Once you’ve done that, it’s time to branch out and become proficient in more complex topics, such as NLP, DL, and RL (Reinforcement Learning). Mastery in any of these fields will make you an expert in that specific domain.


The salary and total compensation of Machine Learning Engineers in countries other than India vary based on four factors, as mentioned earlier. In the US, the median salary for a Machine Learning Engineer is USD 120k, while in the UK, the average salary is GBP 50k.

#Code for Machine Learning Engineer Salary across countries
us_median_salary = 120000
uk_avg_salary = 50000

Tips for Machine Learning Interview Preparation

//placeholder for potential code optimizations or examples

Machine Learning Engineering: A Lucrative Career Option

Machine learning engineering offers a lucrative career in sectors such as banking, finance, transportation, retail, healthcare and more. The pay scale is highly competitive and grows exponentially with experience.


The salary of a Machine Learning Engineer in India varies depending on factors such as experience, company size, skills, and location. In tech hubs like Bangalore, Hyderabad, Chennai, and Pune, the salary can start at around 6.5-7 lakh per annum and can increase exponentially.

Is Machine Learning a Well-Paid Field?

Definitely! If you start at an entry-level position and show willingness to learn and adapt, machine learning can lead to enormous financial rewards in the long term.

Is Machine Learning a Good Career?

In the rapidly expanding job market, automation is highly desirable. Machine learning is often the foundation used to solve these automation problems. Therefore, its importance is comparable to salt in food, making it an excellent career choice.

Salary Comparison: Data Scientist vs Machine Learning Engineer

While there is overlap between the roles of Data Scientists and Machine Learning Engineers, it can be difficult to determine which position earns more. Typically, salaries for both positions are highly competitive and comparable.

Is It Difficult to Become a Machine Learning Engineer?

Learning new skills always requires time and effort, and becoming a Machine Learning Engineer is no exception. However, it is not difficult if you are dedicated to learning and practicing consistently. Patience and perseverance are key in the learning process.

Additional Resources

Here are some helpful resources related to Machine Learning and Data Science:

  • Best Machine Learning Courses
  • Machine Learning Interview Questions
  • Machine Learning MCQ
  • Machine Learning Projects
  • Types of Machine Learning
  • Machine Learning Books
  • Machine Learning Applications
  • Best Data Science Courses
  • Deep Learning Vs Machine Learning
  • Data Science Vs Machine Learning
  • Artificial Intelligence Vs Machine Learning

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