AI engineering is rapidly emerging as one of the fastest-growing roles in the U.S., driven by the proliferation of generative AI (GenAI) applications. As organizations navigate the complexities of operationalizing their GenAI use cases, the demand for skilled AI engineers is set to soar. The nondeterministic nature and unique challenges posed by GenAI require specialized expertise, making AI engineers indispensable. According to Gartner, the number of open positions for AI engineers is expected to triple compared to those for data scientists by 2028. AI engineers will play a crucial role in the continuous feedback loop essential for enhancing the validity, accuracy and scalability of AI workloads.
What Is the Role of an AI Engineer?
The role of an “AI engineer” is not yet widely recognized across most enterprises and is often confused with roles such as data scientist or data engineer. Data engineers focus on building and maintaining data infrastructure and tools, while data scientists analyze and interpret data to derive insights and inform decision-making. In contrast, AI engineers are responsible for developing, implementing and monitoring AI solutions, with a strong emphasis on ensuring production readiness and continuous improvement. In the context of generative AI (GenAI), AI engineers play a critical role in experimenting with and scaling applications from end to end.
AI engineers are expected to possess a wide array of technical and nontechnical skills that are multifaceted and often challenging to acquire over a short period of time. Some of the key technical skills that are essential for AI engineers are:
- Programming and Computer Science: A strong foundation in software programming is essential, with Python being the most commonly used language in machine learning. Proficiency in R, Java, Julia, C++, or C# is advantageous.
- Software Engineering and DevOps: AI development involves agile principles and a DevOps approach, known as “ModelOps,” for continuous integration/continuous delivery (CI/CD) of AI applications. Therefore, knowledge of reliability engineering principles is crucial.
- Distributed Systems/Big Data: AI projects often involve large datasets on distributed systems. AI engineers should have practical knowledge of AI hardware like GPUs and experience with systems such as Apache Spark and Kafka. Familiarity with public cloud IaaS/PaaS and hybrid/multicloud architectures is important.
In addition to technical expertise, AI engineers must possess several key nontechnical skills that are crucial for their success.
- Collaboration is paramount, as AI engineers must work closely with various roles across business, data science and IT. Harmonizing these relationships is essential to effectively integrate AI solutions into organizational processes.
- Communication skills, both oral and written, are also vital. AI engineers need to articulate how they transform scripts into deployable software and explain their optimization techniques, such as parallelizing and tuning, to diverse audiences.
- Continuous learning is another critical skill. AI adoption is an ongoing journey, and staying updated with market developments is essential. A mindset geared toward continuous learning ensures AI engineers remain at the forefront of technological advancements and can adapt to evolving industry needs.
The need for AI engineers will continue to grow steeply in the near term as more enterprises use AI techniques to gain competitive advantage. Technology innovation leaders should advocate for the creation of this role within the enterprise and create a business case for appointing AI engineers.
Author Bio: Arun Chandrasekaran, Distinguished VP Analyst at Gartner
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