Machine Learning Engineer Salary In India 2024

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.

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

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.

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

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	Average Total Compensation
Machine Learning	7 lakh
Deep Learning	7.5 lakh
Natural Language Processing	7.3 lakh
Computer Vision	7.25 lakh
Artificial Intelligence	8 lakh

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