Engineered the migration of equity reference and end-of-day pricing pipelines to a new provider platform, ensuring uninterrupted data delivery for index calculations with zero downtime.

Led the development of a scalable options data platform, defining the data model, building ingestion and monitoring systems, and partnering with product, data providers, and financial engineers to resolve complex data integrity challenges. Delivered a unified data access layer that powers multi-asset index development and self-service analytics across teams.

Transformed operations workflow by developing automated validation checks for end-of-day prices and corporate action data across multiple providers. Enhanced data accuracy and consistency with customizable override capabilities.

Building perception software as part of the acoustic detect and avoid team to answer a specific question, How to tune the current encounter detection models to be resilient to changes in sensor environment, in order to decrease the false positive rate.

Analysed flight data at production nests, to find spatial and temporal correlation for locating probable sources of false positives.

Fine tuned intruder detection models using hard negative samples to reduce false positives by 70% on production nests while having minimal impact on sensitivity.

Developed an end to end OCR Pipeline for the KYC Documents by implementing a robust synthetic data generation pipeline, model validation, hypothesis testing and training modules for text recognition, card detection and text detection tasks.

Light weight reformulation of fixed length text recognition model using the CTC framework and TensorRT for weight quantization to decrease model size by 85\% while maintaining almost the same accuracy level, so that it could be deployed as AWS Lambda service.

Progressively optimized the OCR task specific models bridging the latency vs task specific evaluation metric tradeoff (like word and character accuracy for text recognition).