At Millennium, we have a culture of entrepreneurialism. Our ‘Creative Engineer’ series highlights how technologists bring that mindset to their day-to-day work. In the series, they share more about how they incorporate AI into projects by combining practical technical skillsets with curiosity and experimentation.
Amuthan Kannan, a Bengaluru-based software engineer on the fixed income and commodities technology team, frequently applies creativity to his work. By pairing deep technical skills with curiosity about the business, Amuthan brings outside-the-box thinking to his work, including in his use of AI.
Read more about how Amuthan puts this creativity into practice:
Can you explain your role in one sentence without using the word AI?
I work on the fixed income and commodities technology team, building data discoverability tools that help investment teams quickly find and use the information they need.
How do you apply creativity in your work as an engineer?
Many of the problems we work on may not have an existing playbook or off‑the‑shelf solution. Creativity comes in when I’m figuring out how to adapt new tools and techniques to our legacy systems and data, and then turning those combinations into new, usable tools for our investment teams.
That often means experimenting with different approaches, iterating quickly and finding non‑obvious ways to modernize existing workflows without disrupting what already works.
How can AI help change your approach to solving a challenge?
AI changes both the speed and scope of how I solve problems. It lets me test many ideas quickly, run large‑scale regression and batch tests, and explore elements of complex systems in ways that would otherwise be a very time-consuming manual task.
For example, we can run multiple agents in parallel to probe our own systems and surface behaviors or edge cases we didn’t know existed. In that sense, AI is a real force multiplier for our work — it can increase our output and productivity by an order of magnitude.
How do you use AI in your day‑to‑day work?
In practice, AI helps us help our users understand our systems more effectively. For those new to using the tools my team builds, we have created AI chatbots that give conversational access to product materials which help shorten the learning curve. This allows people to ask questions directly to a chatbot instead of repeatedly going back to the support team or poring over documentation manually.
What’s a common misperception you hear about AI?
A common misperception is that AI replaces human judgment. I believe that it amplifies the judgment you already have — your expertise, your context, and your understanding of the systems you’re working with.
AI can surface options, patterns and information at scale, but deciding what matters and what to implement is still a deep human responsibility.
What’s one technical skill a technologist in the age of AI should master?
I believe a strong grasp of computer science fundamentals and system architecture is important for a technologist. There are often many technically “correct” solutions to a problem, but only a select few might be able to fit into your specific environment.
For example, when I was designing a search algorithm, AI could generate several viable implementations, but only one aligned cleanly with our architecture and technical ecosystem. Understanding those architectural trade‑offs is what lets you guide AI toward solutions that are not just correct, but that could be truly production‑ready.
How can someone become a better user of AI?
Becoming a better AI user starts with understanding what’s under the hood, in my opinion. Learning the core math behind large language models and concepts like retrieval‑augmented generation helps you reason when and how to use the outputs.
Staying up to date with the latest tools and frameworks is also important, but I believe the real way to find success comes from applying them thoughtfully — using AI to automate repetitive parts of your workflow so you can focus on creative elements of enhancing your work.
