In a world of roughly 5,000 language learning model (LLM) researchers but roughly 50 million software engineers, supply constraints have dictated a surge in a new type of professional talent—AI engineers. Their rise is not just a prediction, but an inevitable response to the changing dynamics of the tech world. The emergence of the AI engineer as a new vanguard in technology is a dramatic evolution beyond traditional software development practices, marking the beginning of an exciting new era of AI applications.
The tech role landscape is constantly evolving, and the AI engineer role is rapidly gaining traction. Uniquely located at the intersection of research, application, and development, AI engineers represent a sea change in the way we approach artificial intelligence (AI) and software engineering.
Unlike the traditional roles of machine learning (ML) research scientist, machine learning engineer, and software engineer, an AI engineer combines an understanding of AI models with robust software engineering principles. What are their goals? That is creating scalable, user-friendly AI solutions and services.
Machine learning research scientists create the future of AI primarily by discovering new algorithms or techniques. Their work, while important, usually only appears in academic journals and may not have an immediate real impact on software products.
Instead, machine learning engineers translate these new technologies into viable applications for software products that are more practical than theoretical. At the same time, software engineers use a variety of tools and techniques to build and maintain software systems. While they do integrate AI or ML components into their work, they don't necessarily delve into these technologies.
At this time, AI engineers come in, and they bring an understanding of AI models, similar to machine learning engineers, but more focused on using pre-trained AI models, such as GPT-4 or other basic models. These professionals evaluate different AI models, leverage tools like LangChain or Pinecone, and translate cutting-edge research into practical AI products. Like software engineers, AI engineers understand and apply the principles of robust, scalable, and maintainable system design.
The emergence of basic models magnifies the importance of AI engineers. While AI researchers create these models, it is AI engineers who exploit their full potential through extensive interactions and finding innovative applications in underexplored areas.
2023 is witnessing the increasing importance of human-written code in harnessing and augmenting the power of LLMs like GPT-4. As human engineers become more skilled at AI, AI is increasingly taking on engineering tasks, blurring the lines between them.
As startups receive significant funding to own their own hardware, the role of AI engineers in using models rather than training them will grow. Adopting an "agile" approach to AI that enables AI engineers to quickly and affordably build and validate AI products is gaining momentum.
A major shift is the shift of AI tools from Python to JavaScript, which expands the user base and opens up new opportunities. This change could lead to a fragmentation of the discipline, with AI engineers developing new product suites with a unique set of tools.
As the supply and demand dynamics in the AI field continue to evolve, AI engineers are increasingly becoming the key link between AI research and practical application. Their roles are a testament to the ongoing AI revolution and the growing importance of AI in our daily lives. Their rise underscores a major shift in the tech industry, marking the beginning of an exciting new chapter in technology history.
AI Good Book Recommendation
AI is changing with each passing day, but a high-rise building cannot be separated from a good foundation. Are you interested in learning about the principles and practice of artificial intelligence? Look no further! Our book on AI principles and practices is the perfect resource for anyone looking to gain insight into the world of AI. Written by leading experts in the field, this comprehensive guide covers everything from the basics of machine learning to advanced techniques for building intelligent systems. Whether you are a beginner or an experienced AI practitioner, this book has you covered. So why wait?