Steps to Transition into ML Engineer Role for Software Engineers

Transitioning to ML engineer can be exciting yet challenging. It requires mastering complex math concepts, learning new frameworks, and understanding the technical details associated with ML algorithms. In this article, we look at the relevant skills and the steps required to become an Machine Learning Engineer if you’re a software engineer.

Career aspirants can self-learn to make that transition. However, they can also consider enrolling in a machine learning training course, which provides comprehensive guidance and a structured learning path to acquire the necessary knowledge.

The machine learning course offered by Interview Kickstart, a global leader in career upleveling, is an ideal option aspirants can consider when making a switch to a machine learning career.

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Skills Needed to Transition to ML Engineer Role‍

Software engineers generally have a detailed understanding of software development principles like object-oriented programming structures, dynamic systems development, and version control.

Testing, debugging, and deploying software applications is also a daily affair for them.

Leveraging the knowledge of these concepts can significantly help them to transition into an ML engineer role.

However, there are certain specific skills needed to transition to Machine Learning engineer role:

1. Sound understanding of math concepts including linear algebra, calculus, probability, and statistics.
2. Due to its extensive ML libraries, simplicity, and cross-platform support, expertise in Python programming language is the must-have prerequisite to succeed in transitioning to the ML engineer role.
3. Knowledge of machine learning tools (supervised, unsupervised, and reinforcement learning), and frameworks (TensorFlow and PyTorch), neural networks, CNNs, and RNNs.
4. Experience in writing tests for ML code and data pipelines and deploying models using frameworks like Flask, Docker, Kubernetes, and cloud services is also a relevant skill.
5. Knowledge of big data technologies like Hadoop, Spark, and Presto will be an added plus.

Also read: Basic Python Interview Questions You Should Prepare

Steps to Transition to ML Engineer Role

How to transition from software engineer to Machine Learning engineer role? Many aspirants seeking a career switch would like to have an answer to this question:
Here is the step-by-step guide to transition to the ML engineer role:

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‍Learn the Fundamentals

‍To transition to a machine learning engineer role candidates should start their journey by gathering all fundamental knowledge related to the role of the ML engineer.
They should familiarize themselves with math concepts like calculus, probability, statistics, and calculus. In addition, they should start by developing an understanding of basic machine learning and deep learning principles and techniques.

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Obtain a Relevant Certification

After identifying specific skills required for ML engineer job switch, the next step is to acquire a relevant machine learning certification. Along with providing comprehensive knowledge of all machine learning concepts, such certifications provide hands-on project experience to the candidates.

Ideally, ML engineering career aspirants should look for certifications that offer mentorship from people who have worked in the machine learning field for a long time.

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Gain Practical Experience

‍Attend machine learning boot camps that provide intensive training to further enhance the practical knowledge required to succeed in the ML engineer role.

These boot camps provide knowledge on advanced ML engineering topics including ensemble methods, feature engineering, hyperparameter tuning, big data and scalability, and much more.

Engage With People

‍Engage in conversations with professionals associated with the ML engineer role. Join online communities and connect with people who are eager to share their experiences.

A few online communities are Kaggle and Hugging Face. Try to attend social and professional machine learning events like Data Connect, the International Conference on Machine Learning, and the Collide Data Conference.

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Build a Portfolio

‍Candidates are now ready to build a portfolio to showcase their ML skills including the hands-on projects they’ve worked on as part of the certification course they’ve pursued. 
They should also add information about boot camps they’ve attended to gain both basic and advanced knowledge related to the machine learning field.

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Start Gaining Practical Experience

‍Software developers can leverage their network of friends and acquaintances to get further hands-on experience through freelance projects.

They can also look for machine learning opportunities in their present organization. They can keep a close watch on internal job postings for ML engineer roles as there may be several available.

Further, they can consider taking up internships to gain much-needed on-the-job experience, which may benefit them before applying for more senior positions in the role of an ML engineer.

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Continue Learning

‍AI/ML is a constantly evolving field. To succeed in any associated AI/ML domain one must be proactive in upgrading their knowledge by going through research papers, and attending webinars and conferences.

This commitment to constant learning will keep aspiring ML engineers competitive and ready to make the most of current techniques and technologies at work.‍

Also read: Career Path to Senior Machine Learning Engineer‍

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ML Engineer Salaries

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According to June 2024 data from Indeed, the average ML engineer’s salary in the USA is $160,592 per annum.

Entry-level ML engineers can expect a salary of $104,865 per annum. Senior ML engineers can expect a salary of $245,934 per annum.

The data is curated from 3000 salary sources from different regions of the USA

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Learn AI/ML with Interview Kickstart

‍Interview Kickstart offers different AI/ML courses including machine learning, data science data analytics, and data engineering. 
Those seeking to gain comprehensive knowledge of machine learning operations can pursue MLOps Training course.
IK’s Applied Gen AI course offers all the knowledge one needs to related to building a strong foundation in Gen AI.

Subsequently, they can opt for Advanced Gen AI course to build on their skills.

All these courses offer comprehensive training from instructors who themselves are tech leads and hiring managers at top-tier IT companies, making them ideal tech interview prep courses for any AI/ML aspirant.

You can also read the Interview Kickstart Reviews to get an idea of how IK has helped software professionals land jobs in the ML engineer role

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FAQs: ML Engineer

Is ML Engineer a Good Job?‍

As the demand for machine learning experts is going to grow exponentially in the coming years, an ML Engineer can expect to have a lucrative and rewarding career. Like many other AI-driven jobs, a machine learning engineer earns salaries significantly above the national average.

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Is ML Good for Freshers?‍

The average salary of an ML Engineer is always higher than other IT jobs with better job prospects. Freshers can gain relevant knowledge of machine learning and subsequently start as machine learning interns with an average annual salary of above $62,720 per annum.

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Do ML Engineers Need DSA?‍

Knowledge of data structures and algorithms is essential for machine learning engineers. These computer science topics help ML engineers in problem solving, code efficiency, and overall data analysis.

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Will AI Replace ML Engineers?‍

While AI can replace a few software development jobs it is highly unlikely to affect the machine learning job market. While AI will automate all repetitive tasks, it needs a human engineer’s creativity and problem-solving abilities to perform efficiently.

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