1. What do you currently do, and what’s your favorite part about it?
I am the Founder and AI Engineer at 180GIG Ltd, where I design and deploy production-grade AI applications. My work includes building Squrrel, an AI-powered interview intelligence platform, Trading Flashes, an automated financial intelligence system, and Meal Roaster, a WhatsApp-based AI nutrition assistant. As the sole technical architect, I design the backend systems, AI pipelines, and deployment infrastructure. My favorite part is taking an idea from architecture planning to a live product serving real users. Turning complex AI workflows into reliable systems that operate under real-world constraints is what makes the work meaningful.
2. How did you get started with your Tech Career?
My background was originally outside core engineering, but I became increasingly interested in how automation and data systems could solve real operational problems. I began building backend applications with Python and gradually moved into AI system design. Rather than focusing only on experiments, I pushed myself to deploy systems into live environments. That hands-on approach eventually led me to found 180GIG Ltd, where I now build and scale AI-driven platforms across hiring, finance, and health domains. The transition from learning to building in production shaped my engineering mindset.
3. If Utopia were a color, what color do you think it’d be and why?
I would choose deep blue. Blue represents stability and clarity, which are qualities I value in both systems and society. In engineering, stable architecture and predictable performance create trust. When I build AI platforms such as Meal Roaster, reliability is more important than novelty. A well-designed system should feel dependable and transparent rather than chaotic.
4. If everything about HackerNoon changed drastically, what is one detail you’d like to keep exactly the same? OR What’s your favorite thing to do with HackerNoon and why?
I would want to preserve its focus on builders sharing practical experience. Writing about AI is most valuable when it comes from people who are actively deploying systems. In my own articles, I write about production challenges, explainability issues in financial AI, and real deployment lessons. Maintaining that connection between engineering practice and technical writing ensures the platform remains grounded and credible.
5. Tell us more about the things you write/make/manage/build!
At 180GIG Ltd, I build AI-powered platforms designed for real user interaction. Squrrel is an AI interview system that uses retrieval augmented generation and voice processing to analyse candidate responses. Trading Flashes automates financial signal analysis and newsletter generation using market data and large language models. Meal Roaster operates entirely within WhatsApp, allowing users to send meal images and receive structured nutritional insights without downloading a separate application. Across all products, I focus on scalable backend architecture, AI orchestration, and measurable user impact.
6. What’s your favorite thing about the internet?
The internet enables rapid validation. As a founder, I can design, deploy, and reach users globally without heavy infrastructure investment. This accessibility allows continuous iteration based on real usage data. It also enables open source collaboration, which is how I have contributed to projects such as LangChain. The ability to build publicly and improve through feedback is powerful.
7. It’s an apocalypse of ‘Walking Dead’ proportions, and you can only own a singular piece of technology. What would it be?
I would choose a durable satellite communication device with computational capability. Communication and access to information become the most important assets in uncertain environments. From an engineering perspective, infrastructure resilience always outweighs entertainment or convenience. Reliable connectivity enables coordination and problem-solving.
8. What is your least favorite thing about the internet?
The tendency to reward visibility over technical depth. In AI, especially, there is often more excitement around announcements than around measurable performance. Having built production systems myself, I know how much work goes into reliability, monitoring, and evaluation. Sustainable innovation requires discipline, not just attention.
9. If you were given $10 million to invest in something today, what would you invest in and why?
I would invest in AI evaluation and observability infrastructure. As more companies deploy intelligent systems, monitoring drift, bias, and reliability becomes critical. Strong evaluation layers will determine which AI platforms earn long-term trust. My own experience deploying AI applications has shown that system governance is just as important as model capability.
10. What’s something you’re currently learning or excited to learn?
I am currently refining evaluation pipelines for AI-driven systems. That includes improving consistency, reducing hallucination risk, and optimising latency for real-time applications. I am also exploring more efficient deployment strategies to balance performance and cost. Continuous refinement is essential when building systems that users depend on daily.
11. Would you rather travel 10 years into the past or 10 years into the future? Give reasons for your answer.
I would travel into the future. Observing how AI systems evolve in terms of regulation, trust, and architecture would provide valuable insight. It would help identify which design patterns proved sustainable and which were short-lived trends. That foresight would guide better engineering decisions today.
12. How do you feel about AI?
I see AI as a powerful engineering layer rather than a standalone solution. Its value depends on structured integration, monitoring, and accountability. At 180GIG Ltd, my focus is on building AI systems that operate reliably in production environments across hiring, finance, and nutrition use cases. The next phase of AI will be defined by disciplined architecture and measurable outcomes rather than experimentation alone.
