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The Top 13 Skills Every Machine Learning Engineer Should Have in 2024

June 12, 2024 | Activate Learning | 15 min read

The ongoing investment into artificial intelligence and machine learning offer better, faster ways to process data, serve people, and improve business operations. Skillsoft's IT Skills and Salary Report shows the highest percentage of tech leaders plan to get AI projects off the ground this year — but they're running into some roadblocks. 

For one, recruiting engineers with machine learning skills isn't easy these days. Broadly, recruiting professionals in this domain is tougher than any other, according to Skillsoft's findings. And as if the tech talent wars weren't already raging, the sustained hype around these technologies only fuels the fire. 

What's more, the pace of change — ever hear that before? — is just too fast for learning programs to keep up. There aren't enough skilled workers in the market today who can help all the organizations that have big plans with AI at the center. In fact, tech leaders shared in Skillsoft's IT Skills and Salary survey that their team has some of the widest skill gaps in this area. 

So, tough recruiting conditions mixed with an existing skills gap. It sounds like a rock and a hard place. 

But tech leaders have plans to address the issue and invest in training their teams, augmenting with outside help in the meantime, and even bringing in generative AI to bridge the gaps. A blended approach to closing skill gaps will pay off too, but this begs the question: Which skills do you focus on?

See also: The Top 10 Software Developer Skills for 2024 - Skillsoft

What Are the Top Skills for AI/ML Engineers?

The AI field is fast-moving these days, and that's saying something for an already ceaseless industry. Business and customer demands continue to change as new applications emerge on the market, offering more intuitive ways to interact with or benefit from AI. 

However, none of the awe-inspiring innovation would be possible without those professionals who actually build these applications. Namely, we're talking about AI and ML engineers. And while there are differences between the two, AI and ML engineers sometimes overlap in their responsibilities at work and contribute to the same projects. 

These engineers build and contribute to AI models, systems, or applications that meet business objectives. That could be improving the efficiency of an existing service, or it could mean building an entirely new application. They often work with varying datasets, frameworks, programming languages, and cloud providers. These professionals are ardent problem-solvers, so having the ability to think critically and view challenges from multiple angles can help lead to better outcomes. 

And of course, much of this work is done collaboratively. Often, there's a division of labor between engineers, but they must all come together to share ideas with their immediate team, report progress with the wider organization, and more. Having a strong base of soft skills — skills like public speaking, communication and so on — benefits professionals in this field. 

While the day to day will look different for engineers based on their organization and projects, there are a number of skills that virtually all should have to be successful in their careers. 

These are the top skills AI/ML engineers should have: 

  1. Programming languages
  2. LLMs/Transformers
  3. Prompt Engineering
  4. AI/ML Frameworks
  5. Data Handling
  6. Cloud Services
  7. Containerization, Orchestration
  8. APIs
  9. Monitoring Tools
  10. Collaboration
  11. Communication
  12. Problem-solving
  13. Adaptability

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What's the Best Way to Build These Skills?

The journey to becoming an AI or ML engineer will look different for everyone. And really, the journey never ends. The job itself, given the nature of the work and technology, always changes and requires continuous learning. 

However, it's common for engineers to have started with a formal college degree in computer science. Many job listings require a degree in this field or a similar one — or comparable experience. Today, many universities also offer specific programs or tracks that focus on different facets of artificial intelligence, like machine learning. This trend will likely continue to grow into the future as AI becomes more tightly woven into daily life. 

But what about after graduation? Or what about someone who chose a different major? 

Post-secondary resources are vast today, including many free courses to learn more about this career path, the day-to-day duties, and more. 

AI/ML engineers at Skillsoft frequent the resources below to keep their knowledge and skills sharp, stay current with best practices, keep up with industry news, and more.

Online Courses

As mentioned, several learning and content providers offer a breadth of AI and ML courses. Skillsoft, Codecademy and Global Knowledge all offer technical training solutions that support these career paths, both for individuals and businesses, including some free courses. 

However, many other companies offer resources, including from vendors you may work with on the job, like AWS or Microsoft. Again, some of this coursework is free and it often comes with supplementary material to reinforce concepts or begin applying the knowledge learned in class. 

To start, check this one out: Build a Machine Learning Model | Codecademy

Internal Training

Internal training at work is a great place to support upskilling and reskilling efforts, meaning either building new skills or transitioning into a new role. Explore the types of training available and consider a blend between on-demand and live training.

A note for LMS admins: Learning and development teams should audit and curate their courses in this area to make accessing the training easy — especially given the emphasis tech leaders are placing in this domain. Go a step further to partner with line-of-business leaders to see if your current portfolio meets their demands and team up to spread the word that the training is available. 

Technical Blogs and Research Papers

Reading blogs, research papers, and documentation from leading AI/ML communities also lends value to engineers eager to hone their craft. Engaging with this content enhances individual expertise and contributes to the collective knowledge and advancement of the AI/ML community.

These resources often report on new best practices, offer insight into experiential projects, or share the results of ongoing development. Discussing these reports with peers can lead to fruitful collaborations, new perspectives, and cross-pollination of ideas. 

Workshops and Conferences

For an AI/ML engineer, workshops and conferences offer opportunities to network with peers and industry leaders, gain insights into new research and developments, and participate in hands-on sessions that enhance practical skills. This continuous learning and exposure can significantly advance an engineer's career by keeping their knowledge current and relevant, opening doors to new job opportunities, and fostering innovation in their projects.

What Are the Top Technical Skills AI/ML Engineers Should Have?

The skills an engineer uses day-to-day will change based on the project or task they're focused on. However, as engineers further their careers and become more versatile professionals, it helps to build each of these over time. 

Programming skills 

AI/ML engineers must have strong programming skills to be successful in their careers for several reasons. Programming is the backbone of any AI project. It allows engineers to implement complex algorithms, process data efficiently, and automate tasks. Programming skills also enable engineers to collaborate effectively with other team members, such as data scientists, software developers, and product managers. This collaboration is vital for creating robust and scalable AI solutions.

The programming languages engineers use will change based on their project or employer, but there are some that stand out as worthy investments of time and energy:

  • Python
  • C/C++
  • R
  • JavaScript

Python's simplicity and extensive libraries make it relatively easy to learn, widely applicable, and versatile. Similarly, JavaScript and general web development languages such as HTML and CSS can help progress projects from development to production. 

Overall, it helps to be familiar with the full stack, working with databases and the front end and back end. These skills enable engineers to build intuitive interfaces and deploy their models in real-world applications. 

Resources to help:

LLMs/Transformers

Experience with large language models (LLMs) such as GPT-3.5-turbo, GPT-4, Claude, and more traditional transformers like BERT and all-MiniLM-L6-v2 can enable engineers to build more intelligent, responsive, and adaptable AI systems — faster. 

For AI engineers, having hands-on experience with these models allows them to stay current with the latest advancements in the field, ensuring that they can leverage the most effective techniques for their projects. Moreover, familiarity with both advanced and traditional transformers helps engineers make informed decisions about which model to use based on the specific requirements of a given task, such as efficiency, accuracy, or scalability.

Resources to help:

Prompt Engineering

Prompt engineering involves designing and refining input prompts to obtain the most accurate and relevant outputs from large language models (LLMs). This skill is essential as it enables AI engineers to fully harness the capabilities of LLMs. Understanding when to employ zero-shot, few-shot, and fine-tuning methods can significantly enhance these interactions. By crafting precise and contextually appropriate prompts, engineers can guide the model to generate more useful and coherent responses.

Effective prompt engineering minimizes the need for complex programming, making AI systems more accessible, particularly for learners and non-technical users. The advantages of prompt engineering include improved model performance, faster development times, and reduced computational costs. By optimizing prompts, AI engineers can achieve superior results with fewer resources.

Keep Reading: 

AI/ML Frameworks

AI/ML frameworks are comprehensive libraries that provide tools for developing, training, and deploying machine learning models. These frameworks support functionalities like data preprocessing, model design, and performance evaluation. Two prominent frameworks are PyTorch and TensorFlow.

Engineers use these frameworks to streamline model development. They preprocess data, experiment with different architectures, and train models efficiently. Built-in functions for optimization, loss calculation, and backpropagation let engineers focus on fine-tuning performance. Once trained, models can be easily deployed using the frameworks’ tools, ensuring robust and scalable solutions. Both PyTorch and TensorFlow also offer active community support and extensive documentation, aiding in troubleshooting and learning.

Understanding these frameworks is crucial as each offers unique advantages in AI/ML development.

Resources to help: 

Data Handling

For an AI/ML engineer, data handling involves the efficient storage, retrieval, and management of vast amounts of data essential for training and deploying AI models. Understanding SQL and NoSQL databases is particularly important.

SQL databases like Postgres are relational and use structured query language for defining and manipulating data. They are ideal for handling structured data and complex queries. NoSQL databases, such as Cassandra and Elasticsearch, offer flexibility in data storage. Cassandra is a distributed database system designed for handling large amounts of unstructured data across many servers, ensuring high availability and scalability. Elasticsearch is a search engine based on the Lucene library, optimized for searching and analyzing large volumes of text and unstructured data in real time.

Proficiency working with tools like Postgres, Cassandra, and Elasticsearch enables AI/ML engineers to efficiently manage and analyze data, enhancing the performance and accuracy of AI models.

Resources to help:

Cloud Services

AI/ML engineers must become familiar with AWS, Microsoft Azure, Google Cloud, or other popular cloud providers since they're used to deploy and, just as important, scale machine learning solutions. Scalable machine learning solutions can adapt to growing data and user demands, ensuring consistent performance and reliability. This capability is vital for staying competitive in the market and meeting customer expectations.

A well-rounded understanding of these major cloud providers ensures that professionals can leverage the best tools and services each platform offers. This knowledge allows for greater flexibility in choosing the right cloud environment for different business needs, enhancing efficiency and cost-effectiveness.

Containerization and Orchestration

Containers provide a consistent environment for development, testing, and deployment, ensuring that software runs smoothly across different systems. For these reasons, it's important for engineers to familiarize themselves with Docker and Kubernetes.

Docker simplifies the process by packaging applications and their dependencies into portable containers. Kubernetes takes it a step further by automating the deployment, scaling, and management of these containerized applications. Together, they streamline workflows, enhance scalability, and reduce the risk of configuration errors, making it easier for engineers to focus on building and improving their applications.

Live courses to build skills fast:

APIs

Understanding how to work with APIs allows AI/ML engineers to integrate different systems, enabling them to communicate and function together seamlessly. This knowledge ensures that AI and machine learning models can be effectively embedded into various applications, maximizing their impact. As an engineer, it helps to be familiar with GraphQL and REST architecture.

GraphQL, a query language for APIs, offers a flexible and efficient way to request data. By using GraphQL, engineers can optimize data retrieval, ensuring only the necessary information is fetched, saving bandwidth and processing time. 

REST is a traditional architectural style for networked applications, relying on a stateless, client-server protocol, typically HTTP. RESTful APIs are user-friendly and reliable for integrating services, ideal for creating scalable and maintainable systems. They allow different application components to be developed, deployed, and scaled independently.

Both GraphQL and REST have their strengths. GraphQL's flexibility and efficiency suit complex queries and dynamic data, while REST's simplicity and scalability fit straightforward, robust integration. Mastering both enhances an engineer's ability to build seamless, efficient, and scalable AI/ML solutions.

Resources to help:

Monitoring Tools

Monitoring system performance as an AI/ML engineer involves tracking and analyzing the efficiency and effectiveness of models and systems in real-time. This includes measuring metrics like latency, throughput, and error rates to ensure the models are operating as expected. Tools like New Relic and Splunk help as they provide detailed insights, alerts, and data visualization, enabling engineers to quickly identify and resolve issues, optimize performance, and maintain reliability in production environments.

Technical Skills Are Core to the Job. But What About Soft Skills for AI/ML Engineers?

In addition to technical expertise, non-technical skills — a.k.a, soft or power skills — are playing a pivotal role in the success of AI/ML engineers. In fact, Skillsoft's report found that tech leaders often value a prospective job candidate's soft skills more than their certifications, portfolio or even college degree. 

While technical skills and a proven track record remain of the utmost importance, a well-rounded professional will excel in an environment that changes constantly and requires agility. By honing these soft skills, AI/ML engineers can bridge the gap between technical execution and strategic business goals, fostering a productive and dynamic work environment that drives successful outcomes.

Below are the top soft skills for AI/ML engineers: 

Communication

Effective communication is essential, as it allows engineers to clearly convey complex ideas and technical information to stakeholders, team members, and non-technical audiences. This ensures that everyone involved in a project has a shared understanding and makes informed decisions. 

Collaboration

Collaboration and teamwork are equally important. AI/ML projects often require input from various professionals, including data scientists, developers, and business analysts. Engineers who can work well in diverse teams contribute to a more cohesive development process, leading to innovative solutions that align with business objectives. 

Problem-Solving

Proactive and independent problem-solving skills are crucial for AI/ML engineers as they research, prototype, and select appropriate technologies. These skills help engineers navigate the complexities of AI/ML projects. 

By employing critical thinking, they can assess algorithms and frameworks, choosing the best fit for specific problems. Effective problem-solving enables them to innovate, adapt to new data, and optimize performance. 

Adaptability

The field of artificial intelligence and machine learning evolves rapidly, with new technologies, algorithms, and methodologies emerging virtually all the time. Engineers must be able to pivot quickly and learn new skills to stay current. 

Empathy and adaptability further enhance an engineer's ability to design user-centric solutions that meet real-world needs. Moreover, adaptability allows them to respond effectively to unexpected challenges and changes in project requirements. This flexibility ensures that the solutions they create are not only innovative but also resilient and relevant in a fast-changing technological landscape.

What Does It Take to Be Successful in AI/ML?

Here's the short answer:

  • Continuous Learning: Always be curious and open to learning new technologies and methodologies.
  • Soft Skills: Develop strong communication and collaboration skills to work effectively in a team environment.
  • Problem-Solving: Focus on developing strong problem-solving skills and the ability to think critically.

Pursuing a career in artificial intelligence can be challenging and rewarding. To be successful in the field, begin by establishing a robust foundation in mathematics, statistics, and programming. These core competencies are essential for comprehending and applying AI/ML principles.

Once you have a strong base, select a specialization within AI/ML that captivates you. Focusing your efforts in a specific area will help you to become an expert by keeping you interested and motivated to learn. Continuous learning and adaptation are key to maintaining relevant and competitive skills.

Along the way, you must explore your curiosity of the field and fuel your passion for the work. Doing so will help you overcome bumps in the road and remain focused on what matters most to you as a professional. 

To help progress your journey, enroll in Codecademy's AI/ML Engineer Career Path. It takes 45 hours to complete — give or take — and earns you a certificate to validate your completion. Throughout the path, you'll encounter a mix of projects, quizzes and lessons to learn what it takes to become an AI/ML engineer.