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Embracing the future: Navigating career opportunities in machine learning engineering

Written by: Orlaith O'Mahony
Published on: 7 Jun 2024

Embracing the future: Navigating career opportunities in machine learning engineering

Mpg Embracing The Future 1920

Generative AI has dramatically reshaped the technology landscape. Emerging as a pivotal force following the widespread adoption of popular tools like ChatGPT and Google’s Gemini (formerly Bard), generative AI has had a transformative effect on the entire technology sector.

Whilst it has often been feared that AI will lead to the loss of jobs, we have seen that rather than replacing jobs, in many instances, generative AI is expanding the job market. Particularly in the field of computer engineering, the advancement of AI has seen the creation of several new roles, such as AI data scientists, natural language processing scientists, and, most importantly, machine learning engineers —one of the fastest-growing AI-related professions.

This article explores the machine learning landscape, highlighting how to become a machine learning engineer and the industry trends shaping its future.

What is a machine learning engineer?

A machine learning engineer is at the cutting edge of AI deployment. Often tasked with designing and developing systems that can autonomously learn and improve, machine learning engineers are integral to the future of AI in tech.

In creating algorithms and building models that underpin AI applications, machine learning engineers help turn raw data into actionable insights that businesses can use. Their work spans various sectors, and they are often responsible for driving innovation in healthcare, finance and beyond, making their role critical in today’s tech-driven landscape.

Machine learning engineers integrate AI into usable products and solutions. This vital yet complex role combines data science, software engineering and statistical analysis elements. The significance of a machine learning engineer’s work lies in their ability to solve complex problems and implement solutions that significantly enhance operational efficiencies and boost business capabilities.

How to become a machine learning engineer

Transitioning into a machine learning engineer is an exciting career move for many tech professionals. However, it is not without its challenges.

At Experis, we’re often asked how to become a machine learning engineer. The truth is that the journey often begins with a solid foundation in related fields such as software engineering, data analysis or system architecture. Utilising foundational and transferable IT skills and redirecting them towards AI applications is an absolute must. Many businesses are looking to hire experienced machine learning engineers, so having a wealth of knowledge and experience across IT is essential for standing out as an ideal candidate.

As with any IT profession, education is pivotal in becoming a machine learning engineer. An academic background in computer science or a related field is typically fundamental. Students wondering how to become a machine learning engineer should focus on building or contributing to personal projects incorporating artificial intelligence, machine learning and neural network capabilities.

Technical skills in programming languages like R and Python are also crucial to becoming a machine learning engineer. A robust understanding of frameworks such as TensorFlow or PyTorch can also significantly enhance your hiring chances. Beyond these technical requirements, critical thinking and problem-solving are also vital, must-have skills you should possess.

Industry trends and future opportunities

It is no secret that the demand for talent in the IT sector often outstrips supply. As highlighted in the Q2 2024 ManpowerGroup Employment Outlook survey, 77% of IT organisations reported difficulty finding the needed talent. Thankfully, AI and related technologies such as machine learning and virtual reality are expected to significantly expand operational capabilities and enhance job opportunities.

The AI landscape is constantly and rapidly evolving. As such, it significantly impacts workforce dynamics, with current trends suggesting a considerable transformation in job roles due to the proliferation of AI. The evolution of AI and machine learning underscores the need for professionals to adapt continually and for businesses to support sustainable AI integration with appropriate policies.

Advance your career as a machine learning engineer with Experis

As AI continues to evolve, it offers unique career opportunities for those wishing to become machine learning engineers. Experis supports tech professionals in harnessing these opportunities by connecting them with top tech employers and innovative projects. The growing need for skilled machine learning engineers presents a prime opportunity to advance your career in this field.

Experis is a crucial gateway for tech professionals aiming to move into AI and machine learning engineering, with access to top IT employers and cutting-edge opportunities.

Explore the endless possibilities Experis offers for machine learning engineers and other IT professionals by visiting our website today.