cv ML SWE CV

Masters in Computer Science

• 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

• 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

• 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.

Big Data and Applied AI Engineer

• 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.
• 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

• 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.

Applied Research Scientist Intern

• 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.

Second Place out of 20+ teams at Webex Analytics Datathon 2024

Cisco Systems

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

Cisco Systems

Integrated OpenAI LLM APIs with the Webex Assistant to enable summarization of message threads, media and transcripts (demo). 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

Cisco Systems

Developed the Webex Contextual Search engine using novel conversational representation learning techniques and displayed significant improvement in searching, ranking and recommendations.

Undergraduate Researcher Award

PES University

Awarded for work in the field of Machine Learning.

3x Scholarship Recipient (Prof. CNR Rao, MRD & DAC Scholarship Awards)

PES University

For being in the top 20% among 900+ students.

Finalist at Intel Technovation, Flipkart, IBM, and IISc Hackathons

PES University

Placed among the top 200+ teams.

National newspaper coverage for proposing the model to track garbage collection in Bengaluru

CMR National Public School

Received extensive coverage and recognition for developing an Android app to track and schedule garbage collection in Bengaluru. The currently implemented model was based on our designs and proposals made to the BBMP. Coverage: The Hindu, India Today, Times of India.

Speaker, Guest Lecture on - Building Foundation Models using Transformers

PES University

Delivered a guest lecture to undergraduate students on the advancements in representation learning techniques for language and highlighted the importance of interdisciplinary research.

Teaching Assistant, CS322: Big Data

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.