Spokesperson: Dr Debashis Guha, Director, MAIB and Chair, CRTIB, Director, Master of Artificial Intelligence In Business Chair, Centre for Research on Technology In Business at SP Jain School of Global Management.
The Master of Artificial Intelligence in Business (MAIB) program at S P Jain School of Global Management admits students with varied backgrounds. Some are engineers with experience in domains such as manufacturing, oil and gas, or civil construction, some have IT backgrounds and have worked in Data Science or Artificial Intelligence, while others may be Business or Economics graduates who are new to the field. The kind of career opportunities that are available to graduates of such a program varies quite widely according to the background and experience of each student. Graduates with AI experience can aim for jobs such as Senior AI Engineer, or even Director of AI, while those with domain experience can opt for jobs that involve AI applications in that domain, and freshers will need to look for roles such as junior level machine learning engineers.
The following is a more detailed look at some of these roles.
A Senior AI Engineer’s role is to lead projects to design, develop, and deploy AI models to solve complex business problems. This requires cross-disciplinary collaboration with data engineers, DevOps teams, algorithm builders, front-end developers, business division heads, and other stakeholders to collect and clean data, evaluate and optimize algorithms, and implement and monitor the developed solution. This requires a wide mix of mathematical, computer, AI, and teamworking skills. The job usually requires at least five years of industry experience in developing and implementing AI software using the Python ecosystem, and frameworks such as PyTorch, JAX, or TensorFlow. A Senior AI Engineer role can be a gateway to very senior roles such as AI Director or Vice President, AI. Since the Senior AI Engineers work in collaboration with domain experts, they do not need detailed domain knowledge, although they need to be familiar with the basic concepts and terminology used in the domain.
Machine Learning (ML) Engineer is a role suitable for graduates with IT background, but less experience. ML engineers are responsible for developing and implementing production-ready AI applications. This again requires teamwork with professionals in data engineering, DevOps, and front-end development. ML engineers are responsible for understanding business needs and formulating technical requirements. The job usually requires at least three years of experience in the area. Like Senior AI engineers, they need not be domain experts but need basic knowledge about the domain. ML engineers can expect to go on to become Senior engineers and Directors.
A different kind of job role is suitable for graduates who have experience in a particular domain, such as financial services, or oil and gas, or supply chain management and so on. For instance, an AI Engineer in the Oil and Gas industry often works with niche domain experts such as reservoir engineers, drilling experts, or pipeline engineers, to figure out how AI can help in solving operational issues such as reservoir characterization, seismic interpretation, predictive maintenance, and pipeline design. The requirement for working as an oil and gas AI engineer is a background in petroleum engineering or geophysical exploration, hands-on experience with Python and machine learning frameworks such as PyTorch or TensorFlow, and some knowledge of oil and gas production and exploration.
An entry-level ML engineer’s job is to understand project requirements and translate them into machine learning tasks. They usually work in collaboration with data engineers and software engineers. An entry-level ML engineer works under the guidance of ML Engineers and Senior AI engineers and they will be expected to work on all stages of the ML development pipeline, including data cleaning, feature engineering, model training, and solution implementation. This job requires strong knowledge of Python and its ecosystem, and of machine and deep learning concepts and practices.
Finally, a job that has come into prominence lately, is that of Generative AI Engineer. This is a cutting-edge role, but one that is highly in demand. Gen-AI engineers develop and implement solutions based on Generative AI and Large Language Models. They build large scale deep learning models that can generate text, images, speech, video etc. and use this capability to construct enterprise tools such knowledge assistants or execution agents. This role requires strong skills in Python and deep learning frameworks, large scale data handling, and some knowledge of distributed computing.
