The most important career path today is in the field of Generative Artificial Intelligence (Gen-AI) and Large Language Models (LLM). This is a career path that has emerged only recently but has assumed significant importance in view of the great importance placed upon Gen-AI technology by the AI and technology industry. Engineers in this area implement solutions based on Generative AI and Large Language Models and they build very large-scale deep learning models that can generate text, images, speech, video etc. as well as computer code in various languages such as Python, Rust etc. and SQL. They are also used in more exotic domains such as protein molecule generation or even writing popular songs.
One of the most important roles that has emerged in Gen-AI is using LLMs to construct enterprise tools such as knowledge assistants that can help employees in handling their tasks. Another emerging topic in the area of Gen-AI is the design, construction, and implementation of LLM agents. LLM agents are frameworks based upon LLMs and Large Multimodal Models that can be used to carry out complex projects utilising external functions and tools. A different type of Gen-AI role is hat of a prompt engineer. Although some prompt-engineering jobs are reserved for non-tech domain experts, there is still a pressing need for AI experts that can optimise zero and many shot prompts or iterative prompts using a programmatic approach.
Although most Gen-AI roles are in tech companies as they race to roll out more and more powerful and innovative Gen-AI tools and solutions. However, this is likely to change as non-technology companies start building their own Gen-AI teams that are either focused on specific domains or on solutions targeted on general business needs
A career in Gen-AI requires strong skills in Python and deep learning frameworks such as PyTorch, large scale data handling, and distributed computing. Although most Gen-AI roles are in tech companies as they race to roll out more and more powerful and innovative Gen-AI tools and solutions. However, this is likely to change as non-technology companies start building their own Gen-AI teams that are either focused on specific domains or on solutions targeted on general business needs.
Gen-AI and LLM skills are in wide demand throughout the world. A somewhat different career path is that of a traditional AI Engineer. An AI Engineer’s role is to lead or be a part of large-scale projects that use AI models to solve complex business problems. An AI engineer works with product or functional managers and is part of a multi-disciplinary team that consists of software engineers, data engineers, back-end and front-end developers, and such a project involves full-cycle AI software development that includes data collection and cleaning, algorithm building and testing, and software product deployment and monitoring. This requires a wide mix of mathematical, software, AI, and project management skills. The need for AI engineering talent is higher in advanced economies such as AI and Europe and in development locations such as Israel, Ireland, and India.
A technologically more specialised career path is of Machine Learning (ML) Engineer. These experts develop and implement production-ready AI applications. A senior ML engineer is usually an expert in certain types of algorithms such as ensemble models, or reinforcement learning, or a certain class of tools like anomaly detection or recommendation systems, and they work with product management teams on targeted development projects, or as part of an AI team developing an enterprise solution where they focus on the algorithmic aspects of the solution. They need to have deep knowledge of the Python ecosystem especially those parts that relate to their area of expertise. Some ML engineers may not specialise in a narrow area but acquire knowledge in a wide spectrum of skills and they can become leaders in algorithm choice and testing that is an essential part if model development. ML Engineers are needed everywhere in the world but their demand is likely to be higher in well-established software development locations such as USA, Israel, and India.
All three career paths listed above are located primarily within the technology industry or in the AI department of large enterprises and are focused on technological challenges of building large-scale software tools or more small-scale development that still requires a lot of algorithm development.
Read more: Prompt engineering: A skill born of Gen AI that will be the job of the future
A somewhat different career path is that of data and business analytics. These are roles that require a strong knowledge of Python and AI models, but also require expertise in data collection, cleaning, and visualisation, and the use of these tools to derive business understanding and to guide business decisions. This is a career path that is part of the more general business landscape and not just the technology sector, and is focused not so much on coding algorithms, but more on collecting, exploring, and analysing data and deriving business insights from them.
As more and more enterprises throughout the world become more data-driven, the need for data analytics and business analytics experts is spreading from more technologically advanced locations such as USA to emerging markets such as India, MENA and East Asia.
Guest contributor, Dr. Debashis Guha is the Director of Master of Artificial Intelligence In Business & Chair, Centre for Research on Technology In Business, At SP Jain School of Global Management. Any opinions expressed in this article are strictly those of the author.
