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How to get a Machine Learning Engineer Job in 2021

Category:
Design / Ideas

In the last few years, Machine learning has matured from a vision to reality. We are already living in a world where technology is smart enough to predict outcomes and advanced enough to bring ideas into life. Over one-third of Indian companies have either started or are planning to invest in machine learning technology to automate and optimize their current business processes. Did you know?

It is expected that by the end of 2022, AI will create a business value of approximately $4billion. Seeing this whopping growth in demand for ML technology, the job prospects of machine learning engineers have increased by 330% worldwide in just 3 years (2015-2018). No wonder why ML and AI jobs are among the hottest jobs currently in demand. 

Let’s dive deeper and understand what skills are required, interview preparation guide, and roadmap to get hired as an ML engineer.

Step 1: Learn relevant skills

First things first, before choosing a career path you must be well aware of the time and dedication you have to put into mastering ML technology. Being one of the hottest jobs of the century, the competition is high, so it’s good to gain as much knowledge as you can. A good machine learning engineer has a good understanding of computer programming, math, data analysis, statistics, and subject-specific knowledge.

 

Technologies to master to become an ML engineer

There are plenty of programming languages and frameworks you can choose from to get a good machine learning engineer job. Your choice will decide what you will work on and what you get hired for, so it’s important to choose wisely.

Here are a couple of options you can check before making your choice.

 

  1. Python – Python is used to train ML algorithms. Also, make sure you learn other features available in the language. Moreover, if you want to work on embedded platforms, I suggest you learn the additional language C++. For enterprise environment and data analysis, you can choose Java and R respectively.
  2. TensorFlow/Pytorch –  These two frameworks dominate the market when it comes to deep learning. If you want more of a research role go for Pytorch, and if you want to work for a company and work in a production environment then TensorFlow is the best choice. Overall, the idea is to pick one framework and master it. 
  3. NumPy and Pandas – Efficient Data selection is important when you work with data in python. NumPy has all the basic functions to select data but when it comes to selecting data using advanced criteria, Pandas is the preferred choice. 
  4. OpenCV – If you are interested in computer vision learning then OpenCV is the go-to language for you. It contains many image processing and recognizing functions that can be used to easily detect and localize subjects. 
  5. Apache Spark – Working with loads of data is a must for a machine learning engineer. Here, Apache spark helps you to massively accelerate your efforts in handling big data. Additionally, you can also check Hadoop for data analysis.

Step 2: Apply for a job

When applying for a job interview, you must highlight your relevant experience and past projects to demonstrate your capabilities for the job. Also, make sure your CV is to the point, concise, and lists all the ML frameworks and languages you know.

One important tip is to structure your CV as per the job requirements. Adjust your CV every time you apply for a job to highlight all the relevant skills that the company is looking for. In simple words, your CV should say “I have all the skills and experience required for the position” (obviously, through relevant info).

Step 3: The Interview

An interview is an instrumental process to judge candidates based on their capability and knowledge. No matter how experienced you are every time you go for an interview, you will still likely be a bit nervous, and that is completely fine.

There are many aspects of ML technology an interviewer can ask about. This is why you need to have a good grip on basic algorithms used in Machine learning. Always listen to what the interviewer is asking and then explain your answer in the form of a story. For instance – You explain what situation you were in, tasks you had, actions taken by you, and what the outcome was.

Fortunately or unfortunately, as human minds are built to favor confidence, it’s very important to be confident about every word you say. Here are a couple of tips you must follow during an interview.

Tip 1: Do in-depth research about the company, what it does, clients, current projects, and what it expects from you.

Tip 2: Make an elevator pitch for yourself. This should summarize your relevant experience, skills, and past projects in about a minute.

Tip 3: Usually, the interview starts with some talk by the interviewer. Use this conversation to learn things about the company or the interviewer. This helps you to relate better to the company.

Tip 4: Practice interview questions, Have a friend ask you frequent questions asked in an ML interview. 

Step 4: Coding Task

How can we forget about coding tasks? Especially, for technical posts, the technical round itself is a separate stage used to filter out candidates based on their coding skills. The technical interview round is a great way to showcase what you are capable of when given the time. Here are some tips to tackle problems in the technical round:

Tip 1: Once the task is done, hand-in an additional short report describing the problem, what things you tried, and what worked, and what you could have done if given extra time. This helps the interviewer to understand your strategy to tackle the problem and what additional things you have in mind.

Tip 2: A single problem could have multiple solutions, Especially, for machine learning tasks, always prefer quality over quantity. It’s better to solve a task simply with high-quality output rather than ending up with a difficult solution with low-quality.

Tip 3:  Make sure you adhere to coding guidelines and don’t forget to add documentation to your function. You can also use linter, a tool to analyze and detect potential bugs and code style violations.

Summing Up

Hope this article helps you in getting your dream job. Making a career in cutting-edge technologies like AI/ ML is a lot of fun and also it helps you to keep up with the ever-changing digital world. Working professionals skilled in machine learning technology have several opportunities coming their way to pick from, and that pay well too.

If you are interested in making your career as a successful Machine learning engineer, we, at KritiKal solutions, invite you to join our highly experienced team of AI / ML engineers, solving problems for an array of Big Clients across the Globe.

To apply/check current vacancies, check out our Careers page or you can also drop your CV at hr@kritikalvision.ai.