Can you tell us about your career journey, how you got to where you are today?

I have 19 years of experience, and throughout my career, I have mainly worked for banks. I come from a small-town, middle-class background, and getting into IT happened by chance. But ever since, I’ve stayed in the field.

Over these years, I’ve always looked for opportunities and asked, "How can I be a part of that?". Watching others progress inspired me to do the same. I started my career as what we now call a full-stack developer. I worked across multiple stacks of technologies. As my career progressed, I took every chance to upskill. There was a time we shifted to agile methodologies, and I adapted quickly. Then came the early days of data, before terms like "data science" and "machine learning" were common. We started with predictive analytics, using statistics to forecast and drive insights.

From there, I moved into DevOps, RPA automation, dashboards, analytics, data science, machine learning, and now, generative AI. Two things have defined my career: consistently upskilling and actively seeking opportunities.  

Now, I work for IBM, where I head the Technology Pre-Sales team for the Americas. Our generative AI product suite, called WatsonX, includes nine different solutions for AI, workflow orchestration, and more. We connect with clients, run pilots, demonstrate ROI, and show how WatsonX can improve productivity. Once a deal is made, IBM’s expert labs take over development. I’m part of the technology presence, focusing on bridging that early client engagement.

What attracted you to the field and the industry you're working in?

At the start of my career, we dealt with a lot of legacy systems and stagnancy. What attracted me was the constant evolution of technology and market trends. Another aspect was the business value you can deliver. Often, clients have unsaid problems – challenges they haven’t defined. Solving those requires more than just tech knowledge. You need to understand data, domain, compliance, governance, and provide a foolproof solution.

Generative AI is exciting because it simplifies problems that were previously complex. It makes everything faster and more efficient. We have a concept at IBM called “Client Zero”– we create use cases that act as the very first clients for our solutions. That process of building something brand new and seeing it come to life is what keeps me attracted to this field.

What’s something you wish more people knew about working in AI, but might not realise from the outside?

Whenever we talk about AI, there’s always some imposter syndrome. AI is so broad – math, stats, coding, domain knowledge, ethics, compliance, you can’t know everything and that’s okay. The fact that you don’t know everything is an opportunity to explore more. If someone says they know everything, they’re bluffing, because no one can fully know everything in AI.

Also, AI isn’t the solution to everything. As a data leader, you must understand that AI is often just a small part of a much bigger picture. An ML or GenAI model might represent just 5% of a product. The rest includes APIs, UX, safety checks, business logic, and compliance.  

Can you share some key milestones or achievements in your career so far?

I’ll break that into two parts. First, industry evolution. I started as a developer working in .NET, Java, and legacy systems. Around 2013/2014, the industry shifted from six-month SDLC cycles to agile and smaller, faster delivery packets. Every organisation had to adapt. Next was the shift to cloud, then data. We started with DevOps and predictive analytics, moved to machine learning, and now generative AI.

Second, personal growth and community support. When I began, there weren’t many active communities. Today, students have incredible support through events and exposure. I’ve been both a mentee and a mentor. Community engagement has helped me grow beyond my formal job role – it’s a major achievement for me.

What are the most important skills you think you've developed throughout your career?

You need to stay on top of technology, know the market trends, understand your domain deeply, and be a data expert. All of that is important, but when you ask what the most important skill I’ve developed is, I’d say two things.

One is adaptability with the right attitude. Tech stacks become obsolete fast – earlier it took years, now it takes weeks. Even GenAI models can become outdated within weeks. You must constantly learn, unlearn, and relearn, or add a new dimension to what you already know. That has to happen in sync with market demands, not on your own schedule.  

Second, being a woman in tech, I’ve learned to establish that I’m not fragile. In my experience, people are seen as either very productive or out of the league, and women often get treated as fragile in that context.  

In meetings, I make a disclaimer for myself: “I’ll choose the toughest challenge. If there’s a room of ten people speaking, I’m equally responsible to be part of it.” I’ve had to create that narrative for myself. That mindset helped me build anti-fragility. Anyone can face this, but for women, it’s especially important to say: “You are just as much a part of the team and just as responsible as anyone else.” Once you build trust and confidence with those around you, growth becomes easier. Otherwise, you’re constantly seen as someone who needs shielding.

I’ve switched multiple jobs in my career, and I try to put these things into action as soon as possible, so people realise: She adapts quickly and doesn’t take a backseat.

What have been some challenges you faced in your career and how did you overcome them?

The biggest challenge is maintaining work-life balance, especially when you're an ambitious woman. You have your to-do list, but at the same time, you have a life outside of work. There are many stages in a woman’s life, when you get married, become a mum, then your career almost flattens. Many women leave their careers at this point because it’s easier than balancing both, but I always chose to continue, even if slowly.

My son is now a teenager, but I remember when he was very young. Back then, in India, maternity leave was just three months, not six, like it is now. I used to carry my 2.5-month-old baby to the office every day. I worked with him beside me. But I never said, “Career is my second priority.” It’s not.  

The opportunities will be right in front of you. Someone will ask, “Do you want to travel?” and your mind goes, “Can I travel? Should I? Will my family be okay with this?”Women hesitate to say yes unless they feel 100% ready, but opportunity rarely waits. When it strikes, you have to say yes. None of my transitions, from development to data, to cloud, to GenAI, were handed to me. There was an opportunity. I didn’t know the full scope, but I had to learn quickly and make sure my team learned, because I had to deliver.

How do you stay up to date with trends in AI?

Communities help me a lot. I’ve been lucky to be part of many – you develop friendships, peers, and, more importantly, mentorship. I have a lot of mentees, but I also actively seek mentorship. I always try to figure out who I admire, who I can learn from. Even a simple conversation can be a trigger, every interaction, on social media or in networking spaces, can open doors.  

I work with universities – career mentorship, generative AI sessions. Every time I mentor or teach, I take back a lot. When you’re learning for yourself, you can be 50–60% ready. But if you’re teaching, you need to be 200% ready. Giving back helps me upskill and stay informed, because students won’t be happy if you’re not delivering cutting-edge content.

What’s one piece of advice you’ve received that has stuck with you and you’d like to pass along to others?

With all the ups and downs we face, I’ll say: be brave and be humble. When you’re in a down phase in your career, stand with yourself, be brave, face it without fear. When you're experiencing success, when good things are happening, stay humble, calm, composed, and grounded. There are always new skies to reach.

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