Jun 2024 – Aug 2024 · 2 mos · New York City Metropolitan Area · On-site

• Developed a query analytics engine using KDB/Q to optimize prime brokerage resource allocation, reducing processing time by 2+ hours daily through efficient transaction tracking and capacity analysis.

Jan 2024 – Mar 2025 · 1 yr 2 mos · San Diego, California, United States · On-site

• CMRG (Athletic Data Analysis)
• 1. Analyzed Functional Movement Screen (FMS) data using PCA and GMM to identify injury-prone movements, achieving 90% sensitivity and 86% specificity in females, 88% sensitivity and 100% specificity in males.
• 4D Cell (AI Drug Discovery)
• 1. Designed a contrastive learning pipeline (SimCLR, BYOL) coupled with VAE to compress microscopic images to identify mitochondrial phenotypes in cancer cells, achieving 75.8% accuracy across 26 drugs.
• 2. Achieved 20x speed up using GPU parallelization on legacy C++ mitochondrial analysis algorithm mitograph.
• 3. Developing 4D cell segmentation techniques using SAM and SAM2, and a point-cloud-based tracking algorithm to stitch 3D segmentations over time.

Apr 2022 – Aug 2023 · 1 yr 4 mos · Bengaluru, Karnataka, India · On-site

• Worked on Flipkart’s Adtech Platform team handling data and infrastructure challenges:
• 1. Druid Infrastructure Ownership:
• Led the migration of Druid to GCP, integrating Dataproc for ingestion, reducing processing costs by 15%, improving throughput by 36%, and scaling for peak sales.
• Architected a microservice for Ads ETL pipelines, enabling efficient batch and real-time processing, allowing clients to interact solely through a configuration service.
• Optimized SQL queries by transitioning to Druid-native queries, improving resource efficiency and reducing query latency.
• 2. Designed a fault-tolerant ML feature store using Druid and Kafka, enabling real-time feature updates for ranking models and CRM systems with low-latency access.
• 3. Engineered a funneling solution using Hadoop MapReduce to introduce cross-brand ads, reducing excessive query joins, and boosting search ad click-through rates (CTR) by 3-4% and fill rate by 8-10%
• 4. Integrated real-time keyword targeting using Spark, feeding data into a transformer model to improve product ad relevancy and boost fill rate by 4%.
• 5. Resolved cross-DC call issues in Apache Storm for real-time ingestion by implementing a communication abstraction, enabling traffic splitting across two data centers. Monitored live data center additions to debug real-time issues, achieving zero breakouts.
• 6. Scaled distributed ingestion pipelines on GCP to handle 6x traffic(2M RPS, 2 Petabytes per day) during sale events, leveraging Redis caching, Ansible for horizontal scaling, and automated load testing with Locust

Jul 2020 – Apr 2022 · 1 yr 9 mos · Bengaluru, Karnataka, India · On-site

• Worked as a core member of the Strata TCAM Infrastructure team at Arista Networks:
• 1. Led the design and implementation of event-driven broadcast filtering in hardware, addressing producer-consumer mismatches and restart edge cases. Integrated CLI commands for client verification and compliance, ensuring seamless adoption and system efficiency.
• 2. Enhanced TCAM statistics export using TACC by improving design patterns and adding Python tests, increasing maintainability and security.
• 3. Implemented an SDK-level API to optimize in-memory operations, achieving low latency and faster rule processing in TCAM.
• 4. Debugged system-level networking issues with GDB and TCAM statistics, resolving broadcast/unicast faults, and segmentation errors, and ensuring system stability.

May 2019 – Jul 2019 · 2 mos · Bengaluru, Karnataka, India · On-site

• Worked on Ternary content addressable memory (TCAM) infrastructure team:
• 1. Designed and implemented a non-cohabiting test infrastructure in TACC helping the team to test out functionalities in shared memory space in the server-client model.
• 2. Developed a TCAM client that adapts to a new chip infrastructure using the Test-Driven Development (TDD) paradigm.