Want to Become a Machine Learning Engineer? Learn about the Job Description, Salary, Skillsets

According to the U.S. Bureau of Labour Statistics (BLS), employment for machine learning engineers is projected to grow by 22% from 2020 to 2030. A report from LinkedIn noted that machine learning jobs are among the top 10 in-demand jobs in the tech industry. Careers in machine learning are experiencing high demand and are set for substantial growth, making this field appealing to those looking to enter data professions. With machine learning increasingly influencing business decision-making, the potential opportunities for individuals with the right skill set are virtually limitless.

Recent statistics illustrate a promising outlook for the industry; the global machine-learning market is projected to expand from $26.03 billion in 2023 to $225.91 billion by 2030, achieving a CAGR of 36.2%. Pursuing a career in machine learning offers several key benefits, including job security due to sustained demand across industries, substantial growth potential for career advancement, and the opportunity to shape business decisions through data-driven insights. Let us look at the job description, salary range, and skill set required to pursue a career as a machine learning engineer:

What is Machine Learning?

Artificial intelligence comprises machine learning which involves the use of large data sets in providing an algorithm whereby probable customer behaviour, sales forecasts, and other significant occurrences can be estimated. Due to its capability to learn from past data input, machine learning algorithms estimate output and improve the results by continual learning. It affords the detection of these patterns and thus the enhancement of the intelligence of the other predictive models. An ML engineer provides oversight on how the software systems built incorporate artificial intelligence to mimic human thinking with efficiency.

Skill Set Required for an ML Engineer

Machine learning engineers use data and algorithms to design expert cognition systems where algorithms work just like minds. The key skill sets required in machine learning engineers are as follows:

• Computer Science Fundamentals and Coding: Python, Java, and R programming languages are the most important for building machine learning models.
• Data Modeling: Cleansing, formatting, mining, collection, and segregation skills are important when recommending appropriate datasets for analysis.
• Applied Mathematics: Due to the increasing volume of data, algorithms which analyse data and foresee results require Statistical literacy, Calculus, and Algebraic proficiency.
• Algorithms: Knowledge about how the algorithm is developed, flow charts, and stages of the machine learning model is very crucial to enhance the accuracy and output results of the models.
• Problem-Solving: One needs to be very smart and be able to reason while undertaking some difficulties that may arise during data and system modelling.
• Neural Networks: Understandings of neural networks and their computations are useful in dealing with such data and improving on models.

Job Description for an ML Engineer

Machine Learning Engineers are responsible for designing and developing machine learning systems. They implement suitable algorithms, conduct experiments, and keep up-to-date with the latest advancements in the field. Machine learning engineers work with data to create models, perform statistical analyses, and train and retrain systems to optimise performance. These engineers aim to build efficient self-learning applications and contribute to the progress of artificial intelligence.

Responsibilities of Machine Learning Engineers:

• Study and transform data science prototypes.
• Design machine learning systems.
• Research and implement appropriate ML algorithms and tools.
• Develop machine learning applications according to requirements.
• Select appropriate datasets and data representation methods.
• Run machine learning tests and experiments.
• Perform statistical analysis and fine-tuning using test results.
• Train and retrain systems when necessary.
• Extend existing ML libraries and frameworks.
• Keep abreast of developments in the field.

Requirements and Skills of Machine Learning Engineers:

• Proven experience as a Machine Learning Engineer or in a similar role.
• Understanding of data structures, data modeling, and software architecture.
• Deep knowledge of mathematics, probability, statistics, and algorithms.
• Ability to write robust code in Python, Java, and R.
• Familiarity with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
• Excellent communication skills.
• Ability to work in a team.
• Outstanding analytical and problem-solving skills.
• Bachelor of Science (B.Sc), or Bachelor of Technology (B.Tech) in Computer Science, Mathematics, or a related field; a Master’s degree is a plus.

Salary of an ML Engineer in India

Machine learning engineers are poised for good pay prospects in India. According to the data of PayScale, machine learning engineers get around ?8,29,751 remuneration every year, including bonuses and profit sharing between ?3,48,000 to ?20,00,000.

The increase in the demand for machine learning engineers both within and across industries and territories has led to increased salaries, capped by experience, geographical location, and certain expertise. All in all, the machine learning field is well paid starting from the global perspective, even more so, US, and UK rates are significantly higher than those in India. However, the demand for such professionals is still strong in India, where new employees receive between INR 10,19,891 to INR 11,53,051 per year, according to Indeed and Glassdoor.

Wrapping Up

The field of machine learning is rapidly expanding and hence demands more and more of people with expertise in this field. That is why there are numerous opportunities for people with the appropriate set of competencies as machine learning remains an innovative factor in the modern world. It shows that young people, who would like to become machine learning professionals, may expect interesting and well-paid jobs in the future focusing on both technical background and opportunity to design intelligent systems. This dynamic industry growth not only has prospects and a promotion scheme for employees but also makes it possible for industrial professionals to actively contribute to the development of artificial intelligence and technologies based on data analytics.

FAQs

What does a Machine Learning Engineer do?

A machine learning engineer designs, develops and implements machine learning models and systems to analyse data and make predictions.

What makes a good Machine Learning Engineer?

A good machine learning engineer possesses strong analytical skills, proficiency in programming, and the ability to adapt to new technologies and algorithms.

What skills should a Machine Learning Engineer have?

Key skills include expertise in programming languages (like Python and Java), knowledge of algorithms and statistics, data modeling, and problem-solving abilities.

Who does a Machine Learning Engineer work with?

Machine learning engineers collaborate with data scientists, software developers, product managers, and other stakeholders to build and deploy machine learning solutions.

Is it difficult to become a Machine learning engineer?

While it can be challenging due to the need for a solid foundation in mathematics, programming, and data science, with dedication and the right resources, it is achievable.

Which is better - machine learning engineering or software engineering?

Machine learning engineering can be considered a subfield of data engineering that entails the construction of models and algorithms for analysing and predicting data patterns and software engineering deals with creating general software.

How to become a machine learning engineer?

Basic requirements to become a machine learning engineer include a degree in computer science or a related area, experience with machine learning coupled with expertise in programming languages used in the field.