In this blog, in addition to taking a look at the salary earned by a Machine Learning Engineer, we will explore the career possibilities of a Machine Learning Engineer and cover their different job roles, required skills, and other related topics as well.
Every industry works with massive amounts of data. This fuelled the recognition and acceptance of Machine Learning technology and its value in terms of its application and the future that can be shaped with it. Thus, the need for expertise in this domain catapulted, which resulted in it being one of the most popular career choices in the world today.
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Importance of Machine Learning
Machine Learning algorithms are not really new to the scene. However, the automation of complex mathematical calculations for the purpose of big data is only a recent development. Considering that, Machine Learning has increasingly become more effective, quicker, and optimized over the years. Better performance means enterprises can improve their functioning and productivity and, along with it, have better decision-making.
Machine Learning helps businesses expose critical patterns within the data and make better decisions with minimum human intervention. Apart from this, it is ideal for the timely analysis and assessment of organizational strategies and real-time predictions and, in the process, transforming industries. This, in turn, opens up opportunities galore for Machine Learning Engineers.
What does a Machine Learning Engineer do?
Machine Learning Engineers implement programming frameworks and Big Data tools for transforming raw data into scalable Data Science models. They feed the data into the models that are worked on by Data Scientists and also upgrade theoretical Data Science models to production-level models. These models can work with enormous sizes of real-time data. These tasks call for close collaborations with Data Scientists.
Machine Learning Engineers are also capable of building programs that control robots and computers. The developed algorithms allow machines to uncover patterns in the programming data. They learn to understand commands and eventually make intelligent decisions and carry out tasks without human intervention.
Why is the demand for Machine Learning Engineers increasing?
The demand for Machine Learning Engineers is more than Data Scientists.
The global Machine Learning market is predicted to reach US$20.83 billion in 2024 – Forbes
In light of the growing competition in the global market, companies are hiring talented Machine Learning Engineers. The indispensable nature of a Machine Learning job ensures that the professionals working in related roles receive a hefty sum as compensation for their skills.
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Required Skills of a Machine Learning Engineer
In Machine Learning, mathematics is used in various ways as follows:
- Using mathematical formulas for choosing the correct Machine Learning algorithms
- Approximation of confidence levels
- Setting parameters
Some of the important mathematics topics applied in Machine Learning are:
- Linear algebra
- Multivariate calculus
- Different distributions such as Poisson, binomial, normal, etc.
Apart from math, some physics concepts are also applicable in Machine Learning.
Check out our blog on Machine Learning Tutorial now to learn about the concepts involved in the domain!
Fundamentals of Computer Science and Programming
Every good Machine Learning Engineer is expected to have a fundamental understanding of computer science and knowledge in programming. Computer science concepts include:
- Algorithms (dynamic programming, greedy programming, searching, sorting, etc.)
- Data structures (queue, stack, graph, tree, etc.)
- Space and time complexity and more
Programming concepts in Machine Learning include:
One should also be familiar with Python libraries such as Scikit-Learn, NumPy, Pandas, TensorFlow, Matplotlib, etc.
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Machine Learning Algorithms
It goes without saying that Machine Learning algorithms are an essential part of the domain. Some of the common Machine Learning algorithms are:
- Naïve Bayes classifier
- Support Vector Machine (SVM)
- K-means clustering
- Apriori algorithm
- Decision trees and random forests
- Linear and logistic regression
Data Modeling and Data Evaluation
Data Modeling requires the understanding of underlying data structures and discovering patterns that are otherwise hard to notice. Then comes data evaluation, which uses an algorithm that is right for the data. A good Machine Learning Engineer will know everything about the various algorithms that contribute to effective data modeling and data evaluation.
While it is not mandatory to know all the details of neural networks, a core understanding is definitely important for Machine Learning Engineers. Neural networks are modeled after neurons and consist of multiple layers:
- Input layer
- Multiple hidden layers
- Output layer
Data passes through these layers and demonstrates a deep perspective into the parallel computations as well as sequential computations that are further used for data analysis or learning.
Following are some of the different types of neural networks:
- Radial basis function neural network
- Feedforward neural network
- Modular neural network
- Convolutional neural network
- Recurrent neural network
Natural Language Processing (NLP)
Natural Language Processing essentially tries to teach the complexities of human language to computers for them to interpret and understand human communication better. In conjunction with Machine Learning algorithms, NLP builds systems that learn to perform tasks by themselves and get better through experience.
NLP-powered tools can help one identify sentiment, extract named entities from text, and many other things. Language translators, chatbots, spell-checkers, etc. are all examples of NLP applications. Different libraries with different functions form the foundation of NLP.
While soft skills may usually not be considered that important, it makes a world of difference in a career trajectory. As much as it is vital to be able to generate insights from data, it is important to know how to communicate these findings to someone who is not as knowledgeable in the technical field, such as members of other teams, clients, and shareholders.
In a nutshell, here is what a typical Machine Learning Engineer is expected to be skilled in:
- Programming in Java and Python
- Knowledge of math and probability
- Strong understanding of algorithms and statistics
- Software architecture, data modeling, and data structures
- Prior experience with frameworks
- Good communication skills and teamwork
- A degree in computer science, mathematics, or similar fields or equivalent qualification
Roles and Responsibilities of a Machine Learning Engineer
The roles and responsibilities of a Machine Learning Engineer include the following:
- Performing statistical analysis
- Fine-tuning of test results
- Training and retraining of systems
- Working on frameworks
- Undertaking ML experiments and tests
- Designing ML programs
- Building Deep Learning systems for various use cases based on business requirements
- Implementing suitable AI/ML algorithms
Machine Learning Job Roles
Here are the various job roles available for certified experts in the field:
- Machine Learning Engineer
- Data Scientist
- Data Engineer
- Data Analyst
- Software Developer/Engineer (AI/ML)
- Human-centered Machine Learning Designer
- NLP Scientist
- Director of Analytics
- Principal Data Scientist
- Computer Vision Engineer
- Algorithm Engineer
- Computer Scientist
Machine Learning Engineer Job Opportunities
According to LinkedIn, there are over 19,000 Machine Learning Engineer jobs in India alone.
Bangalore: There are 7,000+ Machine Learning Engineer jobs in Bangalore.
Hyderabad: There are over 2,100 Machine Learning Engineer jobs in Hyderabad.
Chennai: There are 500+ Machine Learning Engineer jobs in Chennai.
Mumbai: There are over 1,300 jobs for Machine Learning Engineers in Mumbai.
Pune: There are 1,800+ jobs for Machine Learning Engineers in Pune.
Delhi: 730+ Machine Learning Engineer jobs are available in Delhi.
Check out our blog on Machine Learning Interview Questions And Answers.
Machine Learning Engineer Salary
The average salary of a Machine Learning Engineer in India is about ₹686,220 per year. Following are the city-wise statistics.
Bangalore: According to PayScale, the average salary of a Machine Learning Engineer in Bangalore is ₹855,647 p.a.
Hyderabad: According to Glassdoor, the average salary of a Machine Learning Engineer is ₹898,459 p.a. in Hyderabad.
Chennai: The annual average salary of Machine Learning Engineers in Chennai is ₹671,599 as per PayScale.
Mumbai: The average salary of a Machine Learning Engineer in Mumbai is ₹855,000 per annum as per Glassdoor.
Pune: According to PayScale, the average annual salary of Machine Learning Engineers in Pune is ₹601,373.
Delhi: The average salary of a Machine Learning Engineer in Delhi, according to Indeed, is ₹465,697 p.a.
PayScale Reports on the Machine Learning Engineer Salary in India
Machine Learning salary in India for freshers:
An entry-level Machine Learning Engineer in India earns around ₹506,655 per year.
Junior Machine Learning Engineer salary:
A Machine Learning Engineer in India with 1–4 years of experience earns around ₹675,165 p.a.
Lead Machine Learning Engineer salary:
A mid-career Machine Learning Engineer with 5–9 years of experience receives an average annual income of ₹1,322,574 in India.
Senior Machine Learning Engineer salary:
An experienced Machine Learning Engineer with 10–19 years of experience earns an average salary of ₹1,999,859 annually in the country.
The above numbers are not set in stone. The company, experience, location, and skill—all directly impact the average Machine Learning salary in India. While compared to other countries, the compensation may seem low, judging by the reports, it is still one of the highest-paying jobs in India. So, if you are thinking of taking the plunge, go right ahead as it is the best time for Machine Learning Engineers to thrive.
For more information on Machine Learning, visit our Machine Learning Community.