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I am pretty passionate about Reddit. In a world where media is biased and polarized, Reddit threads gives me a sense of how local communities view the world. It helps me get grounded in reality of what different cadres of people are thinking by browsing different communities. Reddit is very engaging, as in, you can comment and instantly receive feedback. While there is a lot of "internet hate" and "misinformation" in Reddit communities and threads as well, looking at a wider spectrum of communities within Reddit, especially the differing ones, helps you form a less polarized and biased opinion. I prefer contributing by asking open-ended questions and if I have an opinion, posting it respectfully as a question. I also respond to scientific minded questions that I have qualifications to answer. By selectively responding and answering questions with some due diligence, I keep the communities I am a part of thriving and truth-based.

when I was in college robotics club I conducted workshops for line tracing robot using IR sensor. I was assigned as a mentor to a team. I helped them to build that robot from the kit, also helping them understand the concepts behind it. I cleared their doubts.

1. Situation: Our cloud bill had been steadily increasing, but engineering teams lacked visibility into which workloads were driving the cost. Leadership wanted accountability, but SREs like me knew that finger-pointing wouldn’t solve anything without data.

2. Task:I wanted to design a way to give each team ownership of their spend — without adding operational overhead — while also uncovering unused or over-provisioned infrastructure.

3. Action:I built an internal “cost dashboard” that pulled billing data from our cloud provider’s API, tagged resources by team and environment, and visualized it by service, team, and project. But the innovative part was integrating it with our CI/CD workflows: before a PR deploying new infra could be merged, it would estimate the monthly cost impact. I also built a Slack bot that sent weekly team-level cost summaries and flagged idle resources.

4. Result: Within two months, teams identified and shut down $8,000/month in unused dev resources. More importantly, it shifted the culture — infra cost became part of architectural discussions. Finance even started using our dashboard during quarterly planning instead of relying solely on billing exports.

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.

When I joined my new team , I took lead on a new project which was complex in nature, had many unknowns and our team had to work on new framework, techstack, data models. The team was following agile methodology and will start with the work in first sprint. there was no concept of zero sprint or story grooming prior to the start of first sprint.

with the existing approach I envisioned some issues:

1. Because the number of unknown were high, the chances of doing wrong estimations for development were high.

2. There were high chances that the prioritisation is not right and we might have to revisit the features again and again.

3.  We might find out about unknowns while we are in the development process and that might cause delays.

To overcome these issues, I suggested some changes in our approach:

1. Increase the planning for one more release cycle and group the features as now, next and later.

2. For the features planned in the now group , prioritise them for the next release and complete the research and PRDs for them one sprint early. That means dev and QA will have 2 weeks extra to go through the stories, find out the dependencies and share the feedback. This helped in identifying if we need POCs for any feature or we can right away start developing them. Or if further modularisation is required.

3. I suggested to track the features related to new project in a different group, because to begin with the pace of the delivery will be slow.

When we begin, we took one complete release only in the RnD and understanding the new framework and doing small POCs to validate our understanding.

From second release, when we as a team were ready with good understanding on the expectations and other tech dependencies we started delivering and we could complete 2 full flows from end to end.

We had very less amount of spillover and could complete almost everything in the backlog, which was a big issue in earlier releases.

As a program manager it is important that I perform my due diligence in understanding the root cause of the delay and evaluate how delay will be induced in original plan. I will also assess the nature of root cause whether it is a temporary cause which can cause short term impact or a problem that needs to be fixed to ensure sanctity to rest of the program. Long Term Causes could be Testing delays, API unstability, integration failures whereas temporary delays term could be users testing delayed due to business event. Based on this assessment I will create my action plan and communication strategy to all stakeholders. I will revise the program plan and milestones, show variance in plan and record the root cause for same. I set up governance cadence with all stakeholder to update on the impact, analysis and action plan strategy and send a formal communication to bring everyone on board with the situation.

I once led a restructuring initiative aimed at aligning resources with delivery accounts and redesigning KPIs. While the planning and communication strategy were solid, I overlooked whether our data platforms could support the new changes. Just before rollout, I discovered serious data integrity issues. This setback required a major clean-up effort across teams, but we corrected course by prioritizing platform readiness and enhancing our validation processes for future initiatives.