In the current technological landscape, many companies still perceive modern AI Engineering merely as an extension of Machine Learning or Data Engineering. As a result, their job offerings often list requirements aligned with the traditional competencies of these fields.

However, the skills that set apart the top AI engineers from Silicon Valley don’t come from conventional Machine Learning or Data Science programs.

While traditional Machine Learning has focused on tasks such as fraud detection, recommendation systems, and identifying product anomalies, the new AI engineers work on areas like developing text-based applications, personalized learning tools, spreadsheets with natural language processing, or visual programming languages akin to Factorio.

This new AI Engineer profile reflects the evolution of Artificial Intelligence. In this context, Large Language Models (LLMs) play the starring role. The optimization of these LLMs and their integration with many other tools have given rise to a new type of application that goes beyond typical Machine Learning skills. Due to their ability to understand and produce text language or software code, these models have emerged as the key to a new generation of AI applications.

The scope and versatility offered by modern LLMs far exceed the competencies of traditional Machine Learning engineers. It’s only a matter of time before the entire job market recognizes this and adjusts its expectations to this new reality.

Artificial Intelligence (AI) has witnessed rapid growth and constant evolution over the past decades. While previously it was believed that an AI engineer was primarily trained in the fundamentals of Machine Learning and Data Science, the current landscape demands a more diverse and specialized education. Below, we break down the key areas that every aspiring modern AI engineer should consider.

1. Data Science.

Data Science remains essential for any AI engineer. This discipline provides the skills needed to collect, analyze, and interpret large data sets, which are at the core of most AI applications.

2. Machine Learning.

Traditional Machine Learning still plays a vital role. Competencies in supervised and unsupervised learning techniques, recommendation systems, and reinforcement learning are essential.

3. Deep Learning.

With the popularity of neural networks, Deep Learning has earned a prominent place in the world of AI. It’s crucial to understand how neural networks work, how to train them, and how they can be applied to various problems, from image processing to text analysis.

4. Generative Artificial Intelligence and Large Language Models (LLM).

One of the most exciting and revolutionary areas of modern AI is generative, especially in relation to LLMs. These models, like ChatGPT from OpenAI, have proven to be powerful tools for a variety of applications, from chatbots to programming assistants.

5. Application Development with LLMs.

In addition to understanding how LLMs operate, it’s crucial to know how to integrate them into real applications. This includes developing applications based on text and code, personalized learning tools, and systems that leverage natural language processing.

6. Specific Tools and Platforms.

The world of AI is filled with tools and platforms that facilitate the development and deployment of solutions. Familiarizing oneself with platforms like Kaggle, as well as specific tools such as PyTorch, Langchain, and Streamlit, is essential.

7. Software Development and Programming.

Last but not least, AI engineers must be proficient programmers. Mastery of programming languages like Python and related libraries is fundamental. For more information on “software 3.0”, refer to the chapter “AI and Jobs: Opportunities and Threats”.

The training of an AI engineer has evolved to reflect the changing demands and expanding possibilities of the field. Those wishing to enter this realm should be prepared to embark on a journey of continuous learning, adapting to new tools, techniques, and paradigms that constantly emerge. With a solid foundation and an open mindset, the future for AI engineers is bright and full of limitless opportunities.

With the aim of making this training easier, more accessible, and faster for others, at AI Accelera we have designed a formidable offer of online and offline courses for both people with a technical profile and those with a business profile.