As a machine learning (ML) engineer, you will have to build scalable software systems for data science and machine learning applications, to create programs and algorithms that enable machines to make decisions.
Below image depicts the typical workflow of machine learning.
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Machine learning engineer in a nutshell (Source: Linkedin Engineering)
Artificial intelligence is one of the most sought after careers in the world. More than 98,000 jobs posted on LinkedIn list machine learning as a required skill. As per Monster.com, machine learning, NLP and deep learning are the three most top skills in demand.
As machine learning makes huge inroads into various industries, the demand for machine learning engineers is also on the rise. The time is ripe to make a career in the ML/AI field. The AI specialist role has seen a 74% annual growth rate in the past four years.
According to Gartner, at least 50% of enterprise applications will have embedded AI functionality by 2023.
“I’d say about 70% of companies, there’s just those two pieces: operationalization monitoring ops platform management, and also automating some of the drudgery of what data scientists do. What about 30% of companies are doing is sort of adding to the data engineers role,” said Vin Vashishta, CEO of a machine learning strategy consulting firm V Squared.
Further, he said many companies are merging data engineering and machine learning engineering roles by creating an end-to-end platform.
New roles such as machine learning architecture are being created today. As the platform gets bigger, the machine learning engineer should handle the entire architecture and evolve to meet the needs of the data science, machine learning and data analytics organisations.
Skills and background
The most important aspect of an ML engineer is the focus on production and model deployment — not just code that works, but code that functions in the real world, alongside understanding industry best practices to successfully integrate and deploy machine learning models.
For starters, having a computer science, robotics, engineering and physics degree, along with competencies in C, C++, Java, Python, R, Scala, Julia, and other enterprise languages, helps. Plus, a stronger understanding of databases adds weightage.
At experience levels, software engineers, software developers, and software architects are cut out for machine learning engineering roles. “It is almost a straight line from cloud architect to ML engineer and ML architect, as these two roles have so much overlap. If you understand data science and machine learning, you can understand models,” said Vashishta.
As per Payscale, India’s average machine learning engineer salary is approximately INR 686,281($9,382) per year.
Experts believe the pay scale in India is 20% less than the US, where a top-level machine learning engineer would be getting anywhere between $175,000 to $245,000.
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How to prepare for a machine learning engineering interview?
Unfortunately, there aren’t any standards for interviewing a candidate for machine learning engineering roles. However, most companies test for both data science as well as software engineering skills.
If you are a fresher, you could be asked anything from data science to machine learning workflow. The candidates should be aware of different platforms, framework, and tools companies use.
“Having experience is great, but the biggest piece to become a machine learning engineer is about how you handle the implementation, even if it is not exactly what the company uses today, and ensuring them that you have the competency and you can pick up platforms pretty quickly,” said Vashishta.
What if you do not have a degree in computer science?
While the easiest way to become a machine learning engineer is a computer science degree. A few years of experience as a software engineer creates a direct path to become a machine learning engineer.
Attending data science and machine learning boot camps to develop and support machine learning models also helps. “With your software engineering experience, you can walk into an ML engineering role and be pretty successful,” said Vashishta. That applies to anyone who does not have a computer science background or wants to switch from a non-computer science background to software engineering roles.
“The demand for software engineers is just so crazy right now. There are so many of those roles that are easy to crack. With a couple of years of experience, you should be promoted to data engineering, machine learning engineering, etc.,” said Vashishta.
What is the difference between DataOps, MLOps and AIOps engineers?
Companies today are unifying platforms across data science workflow, where they are essentially pulling everything from data, research and model development lifecycle.
DataOps engineers are typically involved in building process-oriented methodology for developing and delivering analytics. MLOps engineers, on the other hand, implement best practices for businesses to run AI successfully with the help of software products and cloud services. AIOps engineers are mainly involved in automating the processes between the engineering teams to work smarter and faster.
However, the ML engineer role moves towards unifying these roles as the platforms to deploy machine learning models are becoming one. “If you are managing the middle and the end of it, why don’t you manage the data aspect too,” said Vashishta.