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Situation: I was tasked with building a prototype of our DIGITS app on the Hololens 2, a mixed-reality device. This was new territory for us, and I was excited to explore how our telephony experience could translate to AR.

Task: My goal was to create a working demo that integrated our telephony SDK into the Hololens experience using Unity and C#. I had about two weeks to get something functional enough to evaluate viability.

Action: I dove deep into learning Unity3D and Hololens development from scratch, and spent considerable time trying to bridge our SDK — which was built in C++/C# — with the Hololens platform. Despite my efforts, I couldn’t get reliable communication between Unity and our SDK.

Rather than give up, I pivoted and simplified the approach by porting our existing Windows desktop app into the Hololens environment. This at least allowed for basic app functionality and UI testing. Through further research and prototyping, I discovered a key platform limitation: Hololens did not support background notifications, meaning incoming calls couldn’t be surfaced while another app was in use.

Result: I documented everything, demoed the prototype to my manager, and recommended that we pause development until the Hololens platform evolved. While I didn’t deliver the full solution I set out to build, I learned a ton, clearly communicated risks, and saved the team from investing more time into a blocked path. I see this as a failure in the original goal, but a success in terms of ownership, problem-solving, and protecting resources.

So for my current role we move in a very fast paced environment, and we brainstorm new ideas everyday on how to make our workflow more efficient and more reliable, which can definitely get overwhelming over time. So I would first assess projects' urgency and impact, and then for each project, I break it down into small milestones. After that I track them on project management tools like Jira to ensure I allocate my time effectively and communicate proactively with stakeholders

I’m my current role, during a core-team meeting I noticed conflicts between 2 team members, the MM and the EE. They were debating about why to include one component in the official BOM and discarding another one.

To understand their perspectives, I spoke with them separately in a friendly environment. The MM was frustrated because the EE didn’t provide any data or argument to discard the component that the MM suggested. This component had the same performance, was pin to pin compatible, and cheaper. On the other hand the EE explained he had bad experiences in the past with the component suggested by the MM and he refused to validate and qualify it to do not waste his time.

So… After being clear about the situation, I brought them together for a discussion and I emphasize to focus on the issue, not in personal discrepancies. Do, during the meeting, I encouraged both to consider the Project requirements, customers need and I worked with them brainstorming session.

Then, they agreed to meet with other stakeholders to get their inputs about these 2 components, they commit on a validation plan including both components. The one suggested by the EE as POR and the one suggested by the MM as alternate. They also agree to allocate 60% of the mass production volume to the EE component and 40% to the component suggested by the MM.

From my side, to ensure progress, I scheduled follow-up meetings with them to monitor their collaboration and for sure, I offered my support. Finally, both components are currently part of the product official BOM and these 2 guys improved their working relationship and communication.

At my previous role, I was leading a fraud detection AI model deployment for a financial services client. The model aimed to reduce fraudulent transactions by 25% while maintaining a low false positive rate.

I was confident in the model’s performance in the test environment, but when we deployed it in production, we noticed an unexpected spike in false positives. Transactions flagged as fraudulent were legitimate, causing disruptions for customers and increasing manual review work. To resolve this, I:

1. Investigated the issue by comparing training and production data.

2. Identified data drift—the model was trained on historical data, but recent transaction patterns had changed.

3. Worked with data engineers to implement real-time data monitoring.

4. Retrained the model with up-to-date data and adjusted hyperparameters to improve generalization.

This fix reduced false positives by 25%, improved fraud detection accuracy, and ultimately saved $500M in operational savings across multiple initiatives. This experience taught me the importance of continuous model monitoring and proactively accounting for data drift. Now, I always incorporate data validation pipelines and feedback loops into AI deployments to prevent similar issues.