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I led a project to implement a new feature in our loan processing system aimed at reducing API latency for faster loan approvals. Midway, I encountered data inconsistencies and latency issues caused by schema mismatches between the new API and our backend services. To resolve this, I optimized the data schema mapping, streamlined query performance, and introduced Redis caching for frequently accessed data, which significantly reduced the API call frequency and improved response times.

I also collaborated with QA to parallelize testing, allowing us to catch issues early. By continuously optimizing our microservices with adjustments to load balancing and asynchronous data processing, we increased processing speeds by 20%. While the deadline was slightly extended, our enhancements directly improved loan approval times and were a solid technical win for both the team and the user experience.

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.

When buildling the next generation instance, during the planning phase I budget the amount of servers we will launch the product with. Given each server cost was over 2 million dollar I biased to towards frugality and provided a bare minimum number of server we could launch with. But what I completely missed was during the developement some servers will end up in bad state and the engineering team will short of servers to test the software and firmware getting built which made meeting the project timeline riskly and heavily dependent on delivery schedules of servers and them staying healthy. This quickly became a bottleneck that I realized. I quickly put togther a plan to request adittional server and provide a detailed breakdown on where and how these would be used during and after developement. Synced with the hardware and ODM team and executives to get a approval and ordered the parts to get the servers and racks build at ODM. 

During my career I manage a large number of projects, but one of the most challenging ones was the development and launch of a Telematic Control Unit for an important car maker in Japan. The project is important to me due to technical challenges we faced, the skills I applied and the impact on the company.

As Program manager my role was to lead a cross-functional team including Design Engineers, SW Engineers, Operations teams, Material procurement teams, quality and logistics, among others to successfully launch the product to mass production.

First, I kicked-off the project. For that, I shared with the team the project scope, the goals to achieve, I shared the project plan with key milestones and defined the roles and responsibilities for the different team members.

During execution, to maintain a clear communication, I set up design review sessions with the entire engineering organization to ensure everyone was aligned and fixing potential issues in advance. Besides, I led a weekly core-team meeting where we assigned tasks to team members, and setting clear deadlines for each task. To make these tasks easier to manage, I made sure tasks were broken down into manageable sizes with clear descriptions and acceptance criteria. This helped the team to stay focus on their deliveries and maintain speed.

Another important part was to manages Risks. I worked with the team to identify and classify possible risk that might impact to the project deliverables. We defined a Risk matrix to monitor during the development process, and also a project board with the main KPIs to achieve during each stage of the project.

To ensure quality, we defined different testing processes focused on the design, and the process. We installed specific quality check for the documents and devices delivered for each of the milestones of the PDP, as well as a clear quality control in mass production.

Finally, for transparency, I reported on monthly basis to our company leaders and customer about the project status, focusing on Timing, cost, quality and scope.

By employing these strategies, we successfully launched our module on time matching with all the requirements defined by our customer. This was a great example of teamworking, and taught me the importance of breaking down large projects into smaller and easy to manage pieces. Transparent and clear communication was also a great lesson learned. This approach helped me to improve in leading teams through complex problems.

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.