4. Algorithm engineer
Algorithm engineers, sometimes referred to as algorithm developers, are tasked with building, creating, and implementing algorithms for software and computer systems to achieve specific tasks and business needs. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design. These engineers are responsible for solving complex computational problems in the organization, often working with large data sets to design intricate algorithms that address and solve business needs. Businesses rely on algorithm engineers to help navigate gen AI technology, relying on these experts to scale and deploy gen AI solutions, consider all the ethical and bias implications, and ensure they’re aligned with all compliance and regulatory requirements. According to the survey, 16% of respondents say they have already hired algorithm engineers to support generative AI, while 31% say they have plans to hire for the role.
5. Deep learning engineer
Deep learning engineers are responsible for heading up the research, development, and maintenance of the algorithms that inform AI and machine learning systems, tools, and applications. Deep learning is a subset of AI, and vital to the development of gen AI tools and resources in the enterprise. This role is responsible for building and maintaining powerful AI algorithms, identifying data requirements, and finding better ways to automate processes in the business to improve performance. Technologies such as chatbots, virtual assistants, facial recognition, medical devices, and automated cars rely on deep learning to create effective products. As companies continue to embrace gen AI, deep learning engineers are critical for businesses that want to capitalize on AI and integrate it into business processes, services, and products. According to the survey, 16% of respondents say they have already hired deep learning engineers to support generative AI, while 28% say they have plans to hire for the role.
6. NLP engineer
Natural language processing (NLP) engineer is a vital role for embracing gen AI in any organization. Gen AI relies heavily on NLP to improve communication and to create chatbots and other AI services that need to communicate effectively with users, no matter the query. This role is responsible for training NLP systems, developing models, running experiments, identifying proper tools and algorithms, and performing regular maintenance and analysis of the models. Candidates typically have experience in big data, coding, model selection and customization, language modeling, language translation, and text summarization using NLP tools. NLP plays a big role in technologies such as text-to-speech (TTS) and speech-to-text (STT), chatbots and virtual assistants, and other gen AI tools that are designed to interact in real-time with users. According to the survey, 15% of respondents say they have already hired NLP engineers to support generative AI, while 27% say they have plans to hire for the role.
7. AI chatbot developer
Chatbots are one of the earliest and most common uses of gen AI in a business setting — it’s very likely that you have interacted with an AI chatbot at some point in the past several years. They help direct customers to the right associates, connect users with important documentation, and can alleviate some of the load on customer service representatives. With gen AI, chatbots are becoming even more sophisticated, with the rise of services such as ChatGPT, Bard, Replika, Cleverbot, and others, which have shown to be powerful tools that are useful to businesses. Chatbot technology is in demand across every industry, and businesses are eager to develop their own chatbot tools to help streamline customer service, appointment scheduling, social media engagement, user support, and even marketing and promotions. According to the survey, 15% of respondents say they have already hired AI chatbot developers to support generative AI, while 27% say they have plans to hire for the role.
8. Prompt engineers
Prompt engineers are responsible for ensuring that tools using gen AI, especially text-to-text and text-to-image AI models, can accurately assess user prompts and deliver the correct information. It’s a role that requires extensive knowledge of natural language processing, coding, natural language queries, and artificial neural networks. Examples of prompt engineering can be seen in tools such as ChatGPT, which takes user queries and generates a unique response, and AI image tools such as Midjourney, which produces unique art and imagery based on user requests. For businesses interested in leveraging AI, especially with chatbots, automated assistants, and image generators, prompt engineering is a vital role to ensure those tools are effective and useful. According to the survey, 11% of respondents say they have already hired prompt engineers to support generative AI, while 26% say they have plans to hire for the role.
9. Chief AI officer
Chief AI officer is a relatively new senior executive position that helps organizations tackle the rapid progress of and demand for AI in the workplace. There are so many considerations when integrating AI into the workplace, especially around security, bias, compliance, and privacy. A chief AI officer is responsible for overseeing AI strategy development by navigating and overseeing the development and implementation of AI in the business. Other responsibilities include overseeing data management and governance, business unit collaboration, ethics and compliance, risk management, talent acquisition and team building for AI, and monitoring overall performance and analytics reporting on AI tools. According to the survey, 11% of respondents say they have already hired a chief AI officer to support generative AI, while 21% say they have plans to hire for the role.
