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What Does a Machine Learning Engineer Do? [Roles & Responsibilities]

HomeBlogData ScienceWhat Does a Machine Learning Engineer Do? [Roles & Responsibilities]

What Does a Machine Learning Engineer Do? [Roles & Responsibilities]

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Ashish Gulati

Published

23rd Apr, 2024

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What Does a Machine Learning Engineer Do?

Businesses are gradually understanding the importance of machine learning and software automation. Hence, a noticeable rise has been seen in the job opportunities for professionals who have pursued a relevant career.

Globally, almost 69 million new machine learning job positions are expected to open up by 2027. At present, the average salary packages are much more lucrative, resulting in more interested candidates applying for these technical roles. But before getting started, aren’t you curious about what does a machine learning engineer do?

These professionals are in extremely high demand in the market because of their core skill sets in the field of information technology. To become one of them, you must gather substantial knowledge in the areas of advanced mathematics, software engineering, and data analytics. Then, as you start working, you will utilize this knowledge to optimize vital ML systems and algorithms. If you are interested in learning more, please continue reading.

What is Machine Learning?

Machine learning is a concept that merges with AI technology, helping computers to learn from existing datasets. These systems are responsible for ingraining complex algorithms into digitized devices and increasing their processing power.

Computers can make more precise predictions when machine learning tools are implemented to program them.

The concept of machine learning first emerged in the 1940s. Even whatever happened back then still continues to be the backbone of many smart tech innovations that are being used in various industries. In the upcoming sections of this blog, you will learn about machine learning engineer roles and responsibilities in detail.

What Does a Machine Learning Engineer Do?

Alexsoft

Machine learning engineers execute different tests by analyzing and organizing huge chunks of data. As they create new machine learning systems, the business processes can run faster as many repetitive tasks get automated. Eventually, they play a vital role in framing business forecast reports.

While exploring dedicated sections for ML engineer responsibilities, you will often see companies demanding the following things:

The selected candidate should know how to train and optimize ML algorithms and models.
They must be able to gauge a system’s performance based on statistical analysis.
The individual must be comfortable working with the latest libraries and frameworks.

You can find that all these aspects are covered within a comprehensive Machine Learning course syllabus. So, once you have registered and started practicing, you are already on your way to achieving greatness on your career path.

Machine Learning Engineer Roles and Responsibilities

After getting hired, you can expect the common duties while getting project briefings. Nevertheless, your responsibility goes beyond mere algorithm implementation as you are promoted to higher ranks. In the upcoming sections, you will get a brief idea about machine learning engineer responsibilities in most organizations.

1. Entry-level

A junior machine language engineer is primarily entrusted with tasks like brainstorming and implementing new AI products. They also work together in teams, with the goal of building and updating software through programming.

Here’s a more detailed breakdown of the responsibilities:

You are incorporated into different ecosystems to learn and upgrade data science prototypes.
You must often collaborate with others to design machine learning systems.
You must attend different client meetings to understand their requirements and build applications accordingly.
Finally, you become part of experiments and test the programs to fine-tune them for better results.

Most importantly, you will play a vital role in extending the current frameworks and libraries. For this, you should have robust coding skills in languages like Java, R, and Python.

Looking for where to learn to code?

You can consider checking out the advanced KnowledgeHut Machine Learning course syllabus. By enrolling in this program, you will be able to work on real-world projects and build an attractive portfolio for an entry-level applicant. Also, the learning path will be monitored to ensure you are on track throughout the course.

2. Mid-level

You can expect your duties to expand as you grow as a machine learning engineer or software engineer. If you are unaware of what a machine learning engineer does after completing roughly 3 years as a professional, here are the details to focus on:

You will be liaising with the top-level managers to solve case-specific business issues.
Sometimes, you may have to step up for a junior member to clean and transform data sets to make them usable in ML models.
You will be responsible for algorithm selections for emergency projects involving time and space complexities.
Most importantly, you must be willing to work closely with a band of other data engineers and stakeholders.

To ensure quick promotions from this stage, the only way is to keep yourself updated on the latest trends in machine learning. Additionally, you should collect information like what do machine learning engineers do in other similar firms to secure a salary hike.

You can check out Data Science course fees for online classes after getting logical responses. Most of the time, these curricula are beneficial as one can study while continuing one’s job.

3. Senior-level

As a senior-level ML engineer, you must work in a broader domain. You will become a seasoned professional in statistics and machine learning algorithms by now. So, the managers will depend on you to finalize the launch of full-fledged scalable applications.

Your job will now primarily be debugging machine learning systems and eliminating abnormalities.
You will lead the teams in implementing effective data mining techniques.
You must work closely with the UX team to realize engaging outlooks for real-time systems.
Finally, you will conduct training sessions for subordinate employees to inculcate company values.

Additionally, you can check a recruiter’s website to understand what a machine learning engineer does in that company.

What Makes a Good Machine Learning Engineer?

We recommend you focus on these pointers to excel in your career as an ML engineer:

First, companies will evaluate your capability of picking appropriate data sets. Thus, you should be good at it.
Next, you should learn to perform statistical analyses in minimum time frames.
Finally, you should have great research skills and a learning attitude to constantly develop excellent ML algorithms and tools.

All of these combined contributed to the progression of a machine learning expert’s career.

Required Skills to Get Hired for an ML Engineer Role

Medium

To become a great machine learning engineer, you should know the on-demand skills in the market. To fulfill the ML engineer roles and responsibilities in any sector, you must acquire:

Advanced mathematical and statistical skills
Strong teamwork, problem-solving, and analytical abilities
Core programming skills across many languages (some popular languages include JavaScript, Python, Java, R, C++, etc.)
Adequate experience working with varied ML frameworks
Sufficient knowledge of cloud technology services like Amazon Web Services (AWS)

Building these skills and securing relevant certificates will open new opportunities for a data science and AI enthusiast.

Machine Learning Engineer Job Requirements[5]

These are some standard pointers that you will come across in a machine learning engineer’s job requirement sheet:

The ideal candidate must have fundamental knowledge of the business.
They should’ve impressive research skills to coin the required data types.
Next, the person should prepare and organize the data in a useful format.
Lastly, they must deploy machine learning models to resolve real-world issues.

Conclusion

If you’ve reached the end, you likely understand what a machine learning engineer does well. Understanding the ins and outs of this role helps you picture yourself in it, which can guide your career decisions.

As a machine learning engineer, your job revolves around creating and managing systems that use machine learning to analyze data and make informed decisions. You’ll collaborate with data scientists and software engineers to develop and deploy models, fine-tune algorithms, and extract insights from data. This dynamic and exciting role offers opportunities to work on cutting-edge technologies and solve complex problems.

By exploring the machine learning engineer description, you’ll gain valuable insights into your career path and be better prepared to pursue your goals.

Frequently Asked Questions (FAQs)

1. What does a machine learning Engineer do daily?

ML engineers spend the majority of their time understanding clientele needs. Next, they gather data and develop effective software solutions for the underlying problems.

2. What industries typically employ machine learning engineers?

After becoming an expert in machine learning, you can be employed in various sectors. Some top recruiting hubs are from the healthcare, fintech, manufacturing, and marketing sectors.

3. What are some common tools and frameworks used by machine learning engineers?

A few popular tools you will use in machine learning include the Google Cloud Platform, AWS, IBM Cloud, etc. TensorFlow, Keras, and PyTorch are three popular frameworks professionals widely utilize.

4. What are some challenges faced by machine learning engineers in the real world?

Sometimes, as an ML engineer, you may suffer within an organization due to improper training schedules. Additionally, there may be problems related to overfitting, underfitting, unnecessary specifications, etc.

5. What career growth opportunities are available for machine learning engineers?

After completing professional courses in machine learning and building projects, you can start working as a data scientist or software developer. Later on, you will get promoted to a machine learning associate.

Ashish Gulati

Data Science Expert

Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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