Working on Advanced Trading team where my responsibilities are

1) design and implement various features for retail trading.

2) solve scalability related problems.

3) mentoring junior engineers

4) work on refactoring to reduce tech debt

5) interviewing candidates

At Indeed, I built large-scale, ML-driven and event-based systems powering job matching, experimentation, and real-time decision making at global scale.

* Led the design and development of pipelines that convert indexed jobs into hosted jobs, enabling improved user experience, experimentation, and monetization.

* Architected scalable systems processing hundreds of millions of daily events with strong performance and reliability.

* Re-architected ingestion, ML scoring, and routing into loosely coupled, asynchronous services using Kafka, improving scalability and reducing latency by up to 90%.

* Built generic ML platforms that enabled data scientists to deploy and test models in real time, driving faster experimentation and increasing incremental revenue by 30%.

* Delivered a COVID-response MVP for batch ML routing under tight timelines, generating $3M+ in incremental revenue.

* Led Kubernetes adoption and continuous delivery practices, improving deployment speed and developer productivity.

* Mentored engineers and contributed to hiring and technical leadership across teams.

At Bloomberg, I played a pivotal role in enhancing large-scale search and compliance systems that process petabytes of financial data. My work on Bloomberg Vault involved designing distributed indexing systems and solving complex challenges related to data consistency and performance. I also implemented reconciliation systems for corporate email ingestion, ensuring minimal data transfer overhead and robust compliance.