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Demystifying the Role of a Machine Learning Engineer: Skills, Responsibilities, and Career Path

Last updated on: 
February 14, 2024
|
by 
Soham Mehta
The fast well prepared banner
About The Author!
Soham Mehta
Soham Mehta

With more than 56% of organizations adopting Machine Learning in at least one business function, the career scope for the path is blooming. It seeks innovative and fresh minds capable of viewing the world and problems with new and efficient perspectives. Machine Learning engineering career opens the path to achieving your dreams. 

Here’s what we’ll cover:

Who is a Machine Learning Engineer?

Machine Learning is the part of computer science that uses algorithms to analyze and interpret data. It imitates human learning with the goal of improving learning accuracy. A Machine Learning Engineer is a programmer concerned with designing and building software for automatically functioning AI and ML models. They are also responsible for the efficient running of AI systems in the long term while maintaining and improving the AI systems. 

Machine Learning Engineers work with the data science team while collaborating with other teams of IT experts, software developers, DevOps experts, data engineers, and others. They handle the data and build and train models along with the function of their deployment for production in business. 

Roles and Responsibilities of Machine Learning Engineer

To understand what is the role of a machine learning engineer, go through the following points: 

  • Research about the data science and data analytics prototypes while modifying and applying them practically 
  • Develop methods and plans for Machine Learning projects 
  • Find appropriate datasets relevant to the project 
  • Develop  automated processes for data ingestion and preparation 
  • Interpret and employ the test findings through statistical analysis 
  • Train and retrain the Machine Learning model systems 
  • Generate Machine Learning models and applications as per the client’s requirements
  • Improve and broaden the current Machine Learning frameworks and libraries 
  • Investigate, test, and deploy the appropriate Machine Learning tools and algorithms 
  • Evaluate the accuracy and efficiency of Machine Learning algorithms for practical usage 
  • Find the hidden potential in data and share the same with both technical and non-technical members 
  • Identify the discrepancies hampering the model’s effectiveness
  • Provide technical support to the team

Skills Required to Take Machine Learning Engineer Role

The skills essential to the Machine Learning Engineer role are listed below. 

Technical Skills 

  • Operating System: The functionality is required in Linux and Unix with good command over the stated operating systems 
  • Programming Language: It is a must for parsing and preparing the data to be used in Machine Learning algorithms. Common expectations here are Java, C++, and C, 
  • Processors: Familiarity with GPUs and CUDA is an essential skill expected from the employee. GPUs or Graphic Processing Units are used to accelerate workloads while working on large-scale Machine Learning models. CUDA is the programming interface used by GPUs. 
  • Mathematics: Strong ability in applied mathematics with a firm grasp of concepts like multivariate computation, matrix multiplication, linear algebra, probability, optimization, and algorithms are expected from the candidate. 
  • Statistics: Separate knowledge of important concepts of statistics is crucial for data and model analysis and interpretation of the results. Bayesian statistics, ANOVA, chi-square test, t-test, z-test, and others are required. 
  • ML components: Hands-on experience and practical skills in Machine Learning frameworks, libraries, and packages are among the core requirements of the candidate. 
  • Neural Network Architecture: MAchines are trained to perform cognitive tasks. So, the ability to work through neural network architecture is also important. 
  • Natural Language Processing: It bridges the gap between machines and humans. Familiarity with tools and technologies used in this field is necessary. Common examples include NLTK, RNN, and others. 
  • Data modeling and evaluation: Machine Learning Engineers deal with massive amounts of data. So, the evaluation and method to model it align with testing the behavior of final ML models must be known. 
  • Spark and Hadoop: The ML engineering aspirants will face the need to process large datasets through Spark and Hadoop. Hands-on experience with these is a must. 

Soft Skills 

  • Teamwork 
  • Leadership skills 
  • Problem-solving skills 
  • Analytical skills 
  • Communication 
  • Visualizaiton 
  • Observation 
  • Domain knowledge 
  • Time management 

Career Path For Aspiring Machine Learning Engineers

Becoming a Machine Learning Engineer is a matter of choice rather than following a specific path. You can opt to enter and dominate the field at any time of your career by gaining relevant education and skills. Here are important points to follow for aspirants seeking a career transition to Machine Learning Engineering: 

  • Identify the Shortcomings 

It is important to understand what the candidate knows and what they don't. The non-techs can skip the point as they need to begin from scratch. However, the technical background holders can dive in to learn the concepts they already know and the missing gaps they need to fill. For instance, they may know a programming language common in another sector but has lesser significance in Machine Learning, 

  • Get the Education/Certification 

Regardless of the technical background, unless the candidates belong to the same core field or know everything after analyzing the first point, the requirement of education persists. The education or certification programs are important for familiarity with core concepts of Machine Learning, which encompasses algorithms, tools, techniques, programming languages, statistics, and others. 

  • Gain Hands-on Experience 

Mere learning is not enough. Applicability is important in the real world. The candidates must strive to gain hands-on experience with the tools and techniques and application of skills. Seek assistance from the course provider or search for ways to learn the usage and application. Alternatively, join internships and other opportunities to learn and gain hands-on experience. 

  • Build Portfolio

Showcase your hands-on experience and skill application through a portfolio. Ensure that you experiment with other domains while striking a balance in known fields. It exhibits your domain-specific knowledge, making you a possessor of industry-relevant skills. 

  • Networking 

Networking is important in entering any field. Build the connections and exhibit your presence. Conferences, seminars, workshops, online communities, and others are great platforms for the same.

Career Transition to Machine Learning Engineer

Salary Expectations for Machine Learning Engineer

Machine Learning Engineers are paid good salaries in India. Though the exact salary varies among companies, sectors, experience, and other factors, the base pay range is INR 8 lakh to INR 14 lakh. The average salary for a Machine Learning Engineer is INR 12 lakhs. 

The engineers can also expect additional cash compensation beginning from INR 77,500, ranging up to INR 2,50,000. The average additional cash compensation can be expected to be up to INR 2 lakhs. 

Ace Your Machine Learning Engineering Interview with Interview Kickstart

With the expected increase of AI and ML specialist demands by 40% between 2023 and 2027, it is the right time to opt for a career in the field of ML. The candidates must possess the right blend of skills, education, and experience to ace the world of Machine Learning. Do you wish to work hard to accomplish your dreams? Interview Kickstart is the right platform to execute your plans. 

We welcome the possessor of prerequisites to become Machine Learning Engineers to help them crack their interviews. With FAANG+ company recruiters working in core Machine Learning, they know what it takes to crack the interviews at top MNCs. Do you want to know what you lack and what you want to work on? Register for the webinar for free. Do you actually wish to work on your shortcomings? Join our course now!

FAQs About Machine Learning Engineer Role 

Q1. Which companies offer Machine Learning Engineering roles?

Amazon, Quantiphi, Accenture, Tata Consultancy Services, and PHN technology are among the top-rated companies that hire Machine Learning Engineering roles. There are multiple other options to choose from as well. 

Q2. Can I learn Machine Learning without coding?

Though traditional software makes it necessary to be familiar with coding, modern times have the concept of No-code AI/ML. It works on a code-free development platform that acts through a drag-and-drop dashboard to complete the tasks of uploading or importing data.

Q3. Can I become a Machine Learning Engineer without experience?

Gaining experience indicates your ability to work with daily responsibilities and pressure while efficiently handling all the tasks. We recommend getting experience for better confidence, building a strong skill set, and keeping decent salary expectations. 

Q4. How do I get my first machine learning job? 

After gaining education and skills, gain hands-on experience. Build a portfolio expressing your capabilities and experience. Apply for the jobs through online platforms. Keeping patience is the most important task during these times. For interview preparation, join Interview Kickstart to meet the best recruiters there to train you. 

Q5. What is the difference between an AI engineer and an ML engineer? 

Artificial Engineers work on multiple applications that range from robotics to Natural Language Processing. Machine Learning Engineers focus on data-driven applications like classification and predictive analytics.

Posted on 
January 11, 2024
AUTHOR

Soham Mehta

Co-Founder - Interview Kickstart

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