Ayush Gurtu
Master of IT in Business (Artificial Intelligence)
Class of 2026
I began my career with a Bachelor of Technology in Electronics and Communication Engineering from Vellore Institute of Technology, where I developed a strong foundation in problem-solving and analytical thinking. After graduating, I joined Deloitte as a Technical Consultant, working on data-driven solutions, automation, and analytics across multiple client engagements. Those three years gave me valuable exposure to real business challenges—and an important realisation.
The most effective solutions were not just technically sound; they were aligned with business strategy, governance, and stakeholder needs.
That insight motivated me to pursue the Master of IT in Business (MITB) at Singapore Management University. I was looking for a programme that could deepen my technical capabilities in AI and data while sharpening my ability to translate these into meaningful business outcomes. MITB stood out for its strong industry orientation, applied curriculum, and emphasis on solving real-world problems.
The programme played a pivotal role in shaping how I approach technology today. Beyond building technical depth in areas such as data analytics, artificial intelligence, and automation, MITB trained me to think end-to-end—from problem framing and data engineering to model development and deployment. Just as importantly, it reinforced the need to consider scalability, governance, and business impact at every stage.
This shift in mindset became especially clear through hands-on projects. One such project involved building a Smart Applicant Tracking System (ATS) powered by large language models. Instead of relying on traditional keyword-based screening, the system used semantic understanding to better match candidates with job requirements. Working on this project pushed me to think beyond model performance and focus on usability, efficiency, and decision-making value for end users.
Another defining experience was the Machine Learning Engineering module. It fundamentally changed how I approach problem-solving—not just as building models, but as delivering production-ready systems. Through a project on predicting hospital readmission risk, I developed an end-to-end machine learning pipeline, from data processing to deployment and monitoring. This experience highlighted the importance of balancing accuracy, interpretability, and operational efficiency—considerations that are critical in real-world applications.
Beyond the classroom, my internship as an AI Engineer at ASIAPAC Technology (a Keppel company) provided an opportunity to apply these concepts in practice. I worked on building AI-driven solutions, including a digital human avatar for real-time client interaction and intelligent automation tools that improved sales productivity. The experience reinforced my ability to translate complex AI concepts into scalable, business-impacting solutions within a fast-paced, collaborative environment.
The MITB cohort also played a significant role in my learning journey. Coming from diverse professional backgrounds ranging from finance and consulting to technology and operations, my peers brought different perspectives on how technology can be applied across industries. Through group projects and discussions, I learned to communicate ideas more clearly, challenge assumptions, and incorporate feedback—skills that are essential when working at the intersection of business and technology.
I am currently working as an AI Engineer at the National University of Singapore, where I contribute to building scalable, data-driven systems for enterprise and academic use cases. Reflecting on my journey, MITB has helped me move beyond executing technical tasks to designing solutions that create real impact. It has equipped me not just with the tools to build AI systems, but with the perspective to ensure they are meaningful, responsible, and aligned with business needs.
