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As Program Manager my biggest achievement was leading the development of a complex electromechanical module for HP. The project consisted of developing and producing a huge electromechanical module for a high-speed large format printer.

I was in charge of overseeing the entire Product Development Process, from requirements gathering and solution design to development, testing, deployment, and mass production release.

The Key aspects of this project included

1.Strategic planning: I developed a plan including the project Objectives, scope, and deliverables, as well as timelines, milestones, and resource requirements.

2.Stakeholders management: I engaged with key stakeholders such as business leaders, external vendors, and customers to keep them all on the same page, ensure alignment, and address concerns.

3.Risk Management: Identifying potential risks and challenges associated with product performance, compatibility with external modules, material availability, etc.

4.Collaboration and Communication: I facilitated collaboration and communication among cross-functional teams involved in the project, including engineering, operations staff, and materials, to ensure alignment.

5.Quality Assurance: Implementing robust testing processes and quality assurance measures to ensure reliability and performance.

6.Mass production release: Worked with all operational teams to ensure material and factory readiness to launch the program to mass production

Finally, the project was released on time and satisfied all stakeholders, internal and customer. This reinforced the importance of clear and transparent communication between stakeholders to drive successful technical outcomes and foster a collaborative team environment.

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.

I was working on a large scale program that had integration points with Lab and external 3rd party team . We were following a strict timelines and had intially aligned on end to end delivery and various cross coordination effort . since we were dealing with major cross coordination feed files for further processing from 3rd party team we had highlighted the risk to the leadership team but didn't receive a strong follow up on the risk and commitment from the 3rd party organization on the delivery date . This caused us to miss the go live date had to delay . However going forward we had frequent checkpoint calls and also as a backup for testing effort we build mockster and service virutalisation for testing effort so that we can mock the service behavior of the feeds and data that we were supposed to receive from 3rd party team and moved ahead in our schedule .Therefore for all major delivery and critical delivery of the product we assumed variability and defined on the process of backup scenario in case we encounter Andy hurdles / risk

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.

Situation: During the first calibration of Non-Maturity Deposit (NMD) models, I was working with a senior colleague and a consultancy firm to implement these models. At that time, market rates were rising, and the consultancy firm suggested calibrating a key parameter, the "beta," to be highly sensitive to market rates. My manager agreed, but I disagreed, as I believed this high sensitivity could lead to inaccurate estimations in future calibrations.

Action: I engaged in discussions with my manager and the consultancy firm to fully understand their reasoning. They argued that a more sensitive beta could improve management of metrics like Delta EVE and Delta NII, which was valid. However, I pointed out that we lacked sufficient evidence to support such a high beta and that it might cause issues in future estimations. To address the disagreement, I conducted a sensitivity analysis, testing the model under various market rate scenarios.

Result: The sensitivity analysis helped us reach a compromise. We agreed to lower the beta, ensuring more stability in the model's performance. This adjustment allowed for a smoother transition of the parameter, ultimately improving the model's reliability while maintaining good risk management practices.