Machine Learning Engineer - Career, Salary Insights, and Future

We have come so far in technology that things that used to be fiction for us have become a new normal in our day-to-day lives. You possibly wouldn’t have imagined how a robot could work as a waiter in a restaurant to serve a café, right? But today, we have passed all of those barricades. Thanks our innovations and the contributions, that they have given so far are impeccable.

The era of machine learning and artificial intelligence has already begun and believe it or not it has been quite an impressive journey so far and its application has also made a significant impact in our lives. This, implementation of “no-human” has become so popular that companies are now grinding the market in the search for exceptional machine learning engineers. It has come a long way from “Concept” to “Implementation”. So, let’s first understand what a machine learning engineer is in this article, and later we will see some interesting insights like career, salary graph, and future scope. So, let’s begin.

Who's a Machine Learning Engineer?

A Machine Learning Engineer (MLE) is a programmer who bridges the gap between computer science and artificial intelligence (AI). They are essentially the engineers who design and build the software systems that power machine learning models.

To begin your career as a Machine Learning Engineer Python is the first step to start with. To begin your career, a basic understanding of Python is a must, and Python Programming Foundation - Self-Paced will definitely help you to become one. After having enough knowledge of Python, the next step is getting into Machine Learning. Again, Complete Machine Learning and Data Science course ensures that you get quality knowledge with all the required technical concepts.

Nevertheless, Machine Learning Engineers and Data Scientists aren’t very different from each other, they all fall under the same binary tree. Besides this, ML Engineers should have the exceptional capability to handle large sets of data to highlight the right and meaningful output. Machine learning engineers are responsible for designing, building, and fabricating different business models that are based on ML and are capable to cater the problems in any dedicated field/industry. The machine-learning engineers are also responsible for working closely with different business models that include artificial intelligence too and they should possess skillsets in every aspect of model architecture, data pipeline interaction, and metrics interpretation. 

Key Responsibilities of a Machine Learning Engineer

Machine learning engineers play a pivotal role in data-driven projects. Their primary responsibilities include:

• Data Collection and Cleaning: Gathering and preprocessing data to ensure it is suitable for training models.
• Algorithm Development: Designing and implementing machine learning models and algorithms.
• Model Training and Evaluation: Training models on datasets and evaluating their performance using various metrics.
• Deployment and Monitoring: Deploying models into production environments and monitoring their performance to ensure they operate effectively.
• Collaboration: Working with data scientists, data engineers, and other stakeholders to integrate ML solutions into broader applications.

Skills Required to Become a Machine Learning Engineer

To succeed as a machine learning engineer, a blend of technical and soft skills is necessary:

1. Programming Languages: Proficiency in languages such as Python, R, and Java.
2. Mathematics and Statistics: Strong understanding of linear algebra, calculus, probability, and statistics.
3. Machine Learning Frameworks: Experience with libraries and frameworks like TensorFlow, PyTorch, and Scikit-Learn.
4. Data Handling: Skills in SQL, Hadoop, and Spark for handling large datasets.
5. Problem-Solving: Analytical thinking and the ability to approach complex problems methodically.
6. Communication: Effective communication skills to collaborate with multidisciplinary teams and explain technical concepts to non-experts.

Education and Certification

Most machine learning engineers hold at least a bachelor’s degree in computer science, mathematics, statistics, or a related field. Many also pursue advanced degrees such as a master’s or Ph.D. Certifications from recognized institutions, such as Coursera’s Machine Learning by Stanford University or Google’s TensorFlow Developer Certificate, can also enhance a candidate's credentials.

What does Machine Learning Engineer Do?

As stated earlier, Machine learning engineers are like the architects that construct and repair the AI ecosystem. Machine learning models are diverse with them being computer programs that learn from data and transforming them into usable products. Here's a closer look at what they do:

• Design and Develop ML Systems: They also develop architectures that enable the software systems to execute the trained machine learning models. This requires identifying the appropriate tools, recognized programming languages, and the right frameworks for implementing the model.
• Data Wrangling Experts: The Trump model consumes data, and the ML engineers’ task is critical to gather, clean, and transform the data that will feed the models for learning.
• Algorithm Specialists: For data scientists, they might spend more time exploring the new algorithms but an ML engineer must understand how the variety of these algorithms functions to decide on the most proportional algorithms for the job and adjust them to boost potential.
• Deployment and Monitoring: The work you do is not complete once the model is constructed. They utilize the model in the production domain, analyze the results of its operations meaning that a model learns in the real world in order to provide enhanced capabilities in the future.

In short, machine learning engineers are those people who try to translate science fiction into something we can use in practice within our contemporary society. They are people who transform the elaborate formulas and numbers into workable and practical means for estimating, supporting work effectiveness, and identifying issues.

Career Insight For Machine Learning Engineer

Machine Learning can be a turning point in your life if you’re interested in algorithms, data science, automation, etc. Currently, there are hundreds and thousands of opportunities for machine learning entry positions and surprisingly countries like the USA are still facing the scarcity of not having enough ML Engineers. By this you can imagine the demand for ML today, when the demand is high, the scope becomes much more bright in such a field.

The urge for ML has drastically increased since the COVID pandemic and organizations of all sizes are welcoming this technology with open hands and the race to catch the pace is so trending. Companies are now introducing new methods and techniques to acquire more business with advanced technology and this has increased the rapid figure of ML Engineer jobs right from 23% to 31% (between 2012-2022), and the required learning skills jobs for ML are likely to get a huge bump of 75% in the next 5 years. 

Below, is the chart for the ML Engineer job trends that you should definitely look into:

So, the more demand rises, the more competition will get increased and so do the job opportunities and this will proportionally make ML jobs one of the most trending occupations in the tech industry.

Industries Employing Machine Learning Engineers

Machine learning engineers are in demand across various sectors:

1. Technology: Companies like Google, Amazon, and Facebook lead in employing ML engineers to enhance their products and services.
2. Finance: Banks and financial institutions use ML for fraud detection, algorithmic trading, and risk management.
3. Healthcare: ML is used for predictive diagnostics, personalized medicine, and drug discovery.
4. Retail: E-commerce giants leverage ML for recommendation systems, inventory management, and customer insights.
5. Automotive: Autonomous driving and advanced driver-assistance systems (ADAS) rely heavily on machine learning algorithms.

Salary Insight For Machine Learning Engineer

Machine Learning Engineer Salary Statistics (India Specific)

Let's Explore Machine Learning Engineer salaries in India, considering various factors that influence them:

1. Experience Level

• Entry-level (0-4 years): ₹ 7 Lakhs to ₹ 12 Lakhs per annum. Top companies may offer even higher.
• Mid-level (4-7 years): ₹ 9 Lakhs to ₹ 14.5 Lakhs per annum.
• Senior-level (7+ years): ₹ 14.6 Lakhs to ₹ 25 Lakhs per annum. Can reach up to ₹ 18.47 Lakhs for Lead roles.

2. Location

While national averages provide a general idea, salaries can vary significantly depending on the city and specific region. Major metro cities like Bangalore, Hyderabad, and Delhi typically offer higher salaries compared to smaller towns.

3. Industry

The industry employing the Machine Learning Engineer can also influence their salary. Sectors like technology, finance, and e-commerce generally offer higher compensation compared to others.

4. Skills and Specialization

Machine Learning Engineers with additional skills and specializations in areas like deep learning, natural language processing, or computer vision can command premium salaries.

The average salary for ML Engineers ranges between INR 9LPA – 11LPA in India and USD 1,33,000 – 1,42,000 in the USA and for the rest of the world (figures are based on annual compensation including bonus).    

Let's understand the salary insight for better clarity with this graph:

Besides this, salary is directly proportional to experience and knowledge. If you have a good experience with proper knowledge, you can expect a high salary.

Future Scope for Machine Learning Engineer

Overall, Machine Learning Engineers still have a lot of potential for the future due to their high demand and continuously, rising salary rates. Here's a breakdown with some statistics:

• Job Market Growth: The employment opportunities have gone up rapidly in Machine Learning Engineer’s job. From what I have noticed, it has roughly grown to a 350% in the market, and as for companies which post jobs on platforms such as LinkedIn the count has risen to over 35000 from as less as 10000 to 15000 in just a few years. As per existing research, there will be a 75% increase in demand of candidates having Machine Learning profile in the next five years.
• Salary Trends: Remunerations for the completely certified Machine Learning Engineers are high. Currently, the Forbes site estimates the average gross of workers in the United States as approximately one hundred and ninety-nine thousand and seven only seven cents.
• Overall Demand: According to Gartner, the number of mobile / wireless data connections is projected to reach 2. To sum up, it has identified that by 2022, there will be 3 million jobs in Artificial Intelligence and Machine Learning. This shows the availability of vast opportunities and fairly new to the market.

To summarize, this job title of Machine Learning Engineer is popular currently and doesn’t appear to be going anywhere any time soon; additionally, these jobs are well compensated, and the rates of growth are as well. Thus, if you are interested in this field, it will be a great opportunity to build your careers in this and to join this great area of technologies.

Conclusion

These stats and figures are a clear symbol of its dominance in the tech industry for pacing towards revolutionizing the world for a better future. The demand for ML is not going anywhere in the upcoming future for the next 10 - 15 years and it's not wrong to prejudice that this is the new future of a better world of automation and digitalization and from the perspective of a career it's not less than the most glorious profession today.