Aroma Peter Rodrigues
Technologist
Sunday, 3 August 2025
Could you please introduce yourself?
My name is Aroma, and I’m currently a Software Engineer at Microsoft in Redmond, where I work on large-scale backend systems that power GPT and Copilot use cases—specifically around document processing, entity extraction, and scalable NLP services. My work focuses on building multi-tenant systems that process and serve billions of documents efficiently, ensuring both reliability and performance at enterprise scale.
My journey spans fintech, healthtech, and enterprise AI—across India and the U.S.—with a background that combines hands-on engineering, system design, and outreach. Outside of work, I’ve spoken at PyCon conferences across Europe and Asia, and I’m passionate about making technical knowledge accessible and inclusive.
Whats your background?
I did my undergrad in Electronics and Communication Engineering from NIT Warangal, one of India’s premier engineering institutes. I worked in finance and insurance tech early on—roles that taught me discipline and performance under pressure. After a few years, I pivoted toward AI and data systems, which led me to pursue a Master’s in Computer Science from the University of Massachusetts Amherst, where I graduated with a 4.0 GPA.
Along the way, I’ve worn many hats: front-end developer at Intuit, backend developer at JP Morgan, research assistant in psychology labs, and product lead in outreach-heavy roles. I’ve worked on everything from FPGA-based designs to retrieval-augmented generation systems. I’ve also led large-scale student tech festivals, built tech-for-good tools during the pandemic, and mentored junior engineers and students breaking into the field.
Whats your current role?
At Microsoft, I work in the applied AI backend space. My team focuses on multi-tenant systems that process, extract, and serve knowledge from billions of documents. These systems support downstream products like Copilot and entity-based knowledge experiences.
I focus on optimizing performance and reliability across diverse tenants, designing intelligent ingestion pipelines, and ensuring secure, efficient access to relevant information. I work closely with ML, infra, and product teams to balance low latency with high accuracy—especially in retrieval and summarization use cases. Beyond that, I also contribute to architectural planning, cross-team infrastructure patterns, and internal tooling to support developer velocity.
Why have you decided to learn to code?
I didn’t grow up imagining I’d be a software engineer. I was originally drawn to problem-solving in a more hands-on way—working with electronics, building sensors, and experimenting with hardware. But I soon realized that to make these systems do anything meaningful, I needed to learn how to instruct them. That’s how I came to coding.
At first, it was utilitarian—just a tool. But over time, I fell in love with how expressive code could be. It’s a form of translation: turning ambiguous real-world needs into precise instructions that machines can act on. And in that process, it gave me power—not just technical power, but creative and strategic leverage to build tools, analyze systems, and influence outcomes.
Do you think its important to learn to code?
Yes—but with some nuance. I don’t believe everyone has to learn to code in the traditional sense. But I do think computational thinking—understanding logic, abstraction, automation, and systems—is becoming a foundational skill in many industries.
Knowing how to code gives you the ability to test ideas quickly, scale your insights, and automate repetitive tasks. It’s also increasingly necessary for navigating and questioning the digital systems that shape our lives—from recommendation engines to credit scoring algorithms. Whether you're an artist, entrepreneur, or scientist, knowing how tech works—at least to a functional level—puts you in a much stronger position to innovate or intervene.
Do you feel the tech industry is male dominated? How can we encourage more women into the industry?
Yes, especially when it comes to senior roles, decision-making positions, and deep technical leadership. Representation has improved over the last decade, but retention remains a challenge—and the culture often still centers around exclusionary definitions of “technical excellence.”
To bring more women into tech, we need a combination of visible role models, mentorship, flexible pathways, and culturally responsive work environments. We should also normalize nonlinear careers, re-entry programs, and transitions across domains. Encouraging more women to stay in tech is just as important as helping them start. That means funding women-led initiatives, challenging bias in evaluations, and giving credit where it’s due.
Did you struggle being a woman in the tech industry yourself?
Yes. The challenges weren’t always overt discrimination, but subtler forms—being underestimated, being talked over, being excluded from informal mentorship loops, or having my ambition questioned. Early on, I had to work twice as hard to be taken seriously, and that shaped how I prepared for every role and every review cycle.
But I also found community—women, allies, mentors—who recognized the nuances and helped me build confidence and agency. That experience shaped how I mentor now. I believe in opening doors and staying in the room to support others who walk through them.
Whats the most rewarding and most challenging parts of your tech career so far?
The most rewarding part has been building things that scale meaningfully—whether it’s a system that handles billions of documents or a student initiative that turns into an annual tradition. I’ve also found deep joy in mentoring and creating pathways for others, especially those who feel like outsiders to tech.
The most challenging part has been learning to navigate ambiguity without burning out. Tech rewards constant shipping and visible output—but some of the most important work happens quietly, like debugging systems, writing design docs, or mentoring others. Learning to value all types of contribution—and to advocate for that recognition—has been a lifelong learning curve.