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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.

At Auctane, I had a Sr. Engineer who was very harsh with Jr. Engineers in giving feedback. This led to Jr. Eng afraid of approaching with advice and team morale go down and productivity was down by 10%. I had to coach this teammate to not be hostile and instead approach people with Empathy and show ledership in asking the right questions and help them grown in their carrer as well - with this change in attitude, I saw that the team gel much better and the productivity increased overtime. 

At Auctane, when during the re-architecture presentation I presented that we should be using ElasticSearch for advanced search and reporting features - a Sr. Manager from another team challenged this choice and said that it cannot serve, the company had tried to host and operate in our Data center before and failed - I had this data and presented that facts - I told that ELK cluster can scale horizontally and since this time we would be using AWS managed Elastic Search operations would reduce for our team. By preparing for this meeting and having some prior knowledege did help me answer this question and gain trust by the larger team.