cv CV ML SWE

Masters in Computer Science

• GPA: 3.83/4.00
• Concentration: Artificial Intelligence
• Worked as a Research Assistant at CILVR and Computation & Psycholinguistics Lab, advised by Shauli Ravfogel, Jackson Petty, and Tal Linzen.

Bachelor of Technology in Computer Science & Engineering

• GPA: 8.71/10.00 ≈ 3.76/4.00
• Specialization: Machine Intelligence and Data Science
• Received the Undergraduate Researcher Award for my work in the field of Machine Learning.
• Worked as a Research Assistant at the Center for Cloud Computing & Big Data, advised by Dr. KV Subramaniam.

Applied Research Scientist Intern, Redis LangCache

• Architected a two-stage retrieval and re-ranking pipeline for Redis LangCache, achieving a 12.5% PR-AUC and 8% P-CHR AUC improvement over baselines by integrating cross-encoder re-rankers for full token-level interaction.
• Curated and open-sourced LangCache SentencePairs (v1-v3), a large-scale dataset family spanning 1M to 40M examples from diverse linguistic sources, enabling robust fine-tuning of semantic retrieval and re-ranking models.
• Open-sourced LangCache ReRanker v1 and v2 model families comprising cross-encoder variants fine-tuned with ranking and classification objectives, enabling application-specific score calibration for diverse semantic caching use cases.
• Assisted in the fine-tuning and deployment of LangCache Embed v3, a generalist model for semantic retrieval, achieving 13.5% PR-AUC improvement over v2 and outperforming larger general-purpose models even without re-ranking.
• Developed a comprehensive evaluation framework integrated with RedisVL for LangCache customers, enabling systematic analysis of achievable P-CHR tradeoffs, valid cache-hit rates, and operational thresholds before onboarding.
• Quantified retriever bottlenecks and aggressive vs. conservative re-ranking effectiveness by analyzing recall ceilings and re-ranking movement to optimize operational trade-offs and improve cache-hit quality.
• Supported downstream integration and development of LMCache by building prototypes and conducting performance studies with Redis as an in-memory KV store, demonstrating latency and throughput gains.

Research Assistant

• Investigating arithmetic circuit dynamics in LLMs when operators are redefined in-context by analyzing activation representations and attention patterns across transformer layers using Llama-3.3-70B-Instruct.
• Conducting layer-wise analysis of activation geometry using PCA, centroid trajectories, and cluster separability metrics to trace representational evolution under operator semantic redefinition.
• Examining attention circuit reconfiguration at token and head levels to determine whether semantic remapping reuses existing circuits or activates distinct computational pathways.

Research Assistant

• Evaluating LLMs on compositional generalization and instruction synthesis by studying their ability to translate synthetic Context-Free Grammars (CFGs) into conforming strings.
• Analyzing model outputs in few- and zero-shot settings to assess grammatical conformity and uncover generation strategies used during translation.

Applied AI Engineer, Webex Media Quality Analytics

• Instruction fine-tuned LLMs like Mistral and Llama-2 on-prem to enable secure and cost-effective AI solutions such as translation and RAG for engineers and customers, cutting third-party dependency costs by 30%.
• Led the initiative to build a novel pre-training algorithm for conversational data using PyTorch and HuggingFace, achieving a 40% performance gain over standard approaches at benchmark fine-tuning tasks.
• Developed the Webex Contextual Search engine and improved searching, ranking, recommendations, and topic modeling by 75% with <10% increased overhead latency.
• Integrated OpenAI APIs and on-prem LLMs with the Webex AI Assistant for 15M+ worldwide users to add auto-replies, summarization, querying, and action-item extraction to message threads and meeting transcripts.

Applied Research Scientist Intern, Intel (VSG) Research

• Explored Few-Shot Learning Object Detection (FSOD) techniques to reduce catastrophic forgetting in constrained and heterogeneous driving environments.
• Investigated and designed novel representation learning and attention mechanisms to learn inter/intra-object relationships using PyTorch.
• Outperformed existing approaches at the time on base and novel classes by 0.2 mAP and 3 mAP on the Few-Shot India Driving Dataset, a benchmark for FSOD.

Research Assistant

• Compiled and used TailBench to simulate and profile application loads, monitor performance, and analyze results.
• Explored ways to reduce tail latencies in latency-critical applications such as translation and image recognition.

Big Data Engineer, Webex Media Quality Analytics

• Developed and deployed streaming jobs in Scala and Flink to process 1M+ reports/min and compute 1200+ real-time metrics from Calls and Meetings.
• Applied statistical modeling techniques to investigate and report media quality insights to downstream consumers, reducing errors by 30% and analysis time by 15 hrs/week per team member.
• Led the development of real-time (<1 min) auditing pipelines using Kafka and Python to ensure per-minute data consistency between streaming jobs and Iceberg and Pinot data stores, reducing manual effort by >80%.
• Built graphs and dashboards on the Webex Media Quality Analytics Dashboard using Grafana and Kibana to set up alerts and KPIs for 20,000+ clients and customers.

Big Data Engineering Intern, Webex VideoMesh Analytics

• Migrated the Meetings Analytics Engine from Java and Spark to Scala and Flink to scale up to 1M+ reports/min and significantly improve real-time report generation by over 40%.
• Built VideoMesh Developer APIs using Java and globally rolled them out for 30,000+ enterprises with customer-facing applications.

Languages

• Python, Scala, Java, C/C++, R, Groovy, SQL, LaTeX

ML/Stats Libraries

• PyTorch, Tensorflow, HuggingFace, NLTK, pandas, NumPy, scikit-learn, seaborn, matplotlib, plotly

AI/ML Techniques

• Representation Learning, Mechanistic Interpretability, Transfer Learning, Language Models, RAG

Big Data/Cloud

• Hadoop, Kafka, Zookeeper, Spark, Flink, Iceberg, Pinot, Redis, ELK

Frameworks/Tools

• Git, GitHub, Jenkins, Docker, Kubernetes, Flask, Grafana, PSQL, MongoDB, AWS, Linux

• Second Place out of 20+ teams at Webex Analytics Datathon 2024
  • Containerized and deployed a self-sufficient, on-prem and quantized LLM-RAG pipeline to assist engineers with engineering queries and incident resolution.

• Ranked #1 Internationally out of 300+ teams at the Webex IDEA Hackathon 2023
  • Integrated OpenAI LLM APIs with the Webex Assistant to enable summarization of message threads, media and transcripts: https://www.webex.ai/?socialshare=VideoOverlayPlayer
  • Developed thread-related user actions like searching, grouping and sorting across Webex.
  • Assisted in globally rolling out these features worldwide.
• Ranked #1 regionally and Top 20 Internationally out of 300+ teams at the Webex Playtime Hackathon 2023
  • Developed the Webex Contextual Search engine using novel conversational representation learning techniques and displayed significant improvement in searching, ranking and recommendations.

• Awarded the Undergraduate Researcher Award by PES University for my work in the field of Machine Learning.
• 3x Scholarship Recipient (Prof. CNR Rao, MRD & DAC Scholarship Awards) for being in the top 20% among 900+ students at PES University.
• Finalist at Intel Technovation, Flipkart, IBM, and IISc Hackathons, placing among the top 200+ teams.

• National newspaper coverage for proposing the currently implemented model to track garbage collection in Bengaluru.
  • Received extensive coverage and recognition for developing an Android app to track and schedule garbage collection in Bengaluru.
  • Currently implemented model was based on our designs and proposals made to the BBMP.
  • The Hindu: https://www.thehindu.com/news/cities/bangalore/waste-disposal-all-garbage-trucks-to-have-gps-devices/article29906398.ece
  • India Today: https://www.indiatoday.in/cities/bengaluru/story/app-bangaloreans-track-garbage-vehicles-bbmp-gps-fails-1909736-2022-02-07
  • The Times of India: https://bangaloremirror.indiatimes.com/bangalore/others/these-12th-graders-want-app-solutely-no-garbage/articleshow/57619336.cms

• Speaker, Guest Lecture on - Building Foundation Models using Transformers
  • Delivered a guest lecture to undergraduate students on the advancements in representation learning techniques for language and highlighted the importance of interdisciplinary research.

• Appointed a Teaching Assistant for CS322 Big Data course at the Department of Computer Science, PES University.
• Designed and graded coursework, assignments and projects, and delivered hands-on sessions on Hadoop and Spark for a class of 600+ enrolled students for the undergraduate Big Data course.