cv CV ML SWE

Masters in Computer Science

• GPA: 3.78/4.00
• Concentration: Artificial Intelligence
• Worked as a Research Assistant at CILVR and Computation & Psycholinguistics Lab, advised by Jackson Petty, Shauli Ravfogel 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 Subramanium.

Applied Research Scientist Intern, Redis LangCache

• Improved semantic retrieval in Redis LangCache by building novel cross-encoder architectures with late-interaction attention mechanisms, yielding 24% F1 and 18% precision improvements over baselines.
• Curated LangCache-SentencePairs-v1, a large-scale dataset for supervised fine-tuning of sentence embedding models.
• Fine-tuned and open-sourced LangCache Embed v3 and LangCache Reranker v1, achieving up to 28% recall increase and improving cache-hit quality.
• Quantified retriever coverage bottlenecks and reranking effectiveness by analyzing recall ceilings and movement to optimize operational trade-offs and cache-hit precision.
• Developed a comprehensive evaluation framework for LangCache customers, enabling systematic analysis of cache-hit rates, precision, and recall prior to onboarding.
• Supported downstream integration and development of LMCache by prototyping and conducting performance studies with Redis as an in-memory KV store, demonstrating latency and throughput gains.

Research Assistant

• Investigating the evolution and generalization of arithmetic heuristic circuits in LLMs for mathematical reasoning using circuit analysis.
• Examining whether targeted interventions in LLMs at inference can alter reasoning strategies.

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.

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, cutting third-party dependency costs by 30%.
• Built 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+ users to add auto-replies, summarization, querying, and action-item extraction.

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.
• Designed novel representation learning and attention mechanisms to learn inter/intra-object relationships using PyTorch.
• Outperformed prior approaches by 0.2 mAP on base and 3 mAP on novel classes on the Few-Shot India Driving Dataset benchmark.

Research Assistant

• Compiled and used TailBench to simulate and profile application loads, monitor performance, and analyze results.
• Explored techniques 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 to generate media quality insights for downstream consumers, reducing errors by 30% and analysis time by 15 hrs/week.
• Built real-time (<1 min) auditing pipelines using Kafka and Python to ensure per-minute data consistency between streaming jobs and Iceberg/Pinot stores, reducing manual effort by >80%.
• Created dashboards with Grafana and Kibana to monitor KPIs and set alerts for 20,000+ clients.

Big Data Engineering Intern, Webex VideoMesh Analytics

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

Languages

• Python, Scala, Java, C, R, Groovy, Octave, SQL, LaTeX

ML/Stats

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

Artificial Intelligence Techniques

• Representation Learning, Transfer Learning, Few-Shot Learning, Language Models, Natural Language Understanding

Big Data/Cloud

• Hadoop, Kafka, Zookeeper, Spark, Flink, Iceberg, Pinot, 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
  • Containerised and deployed a self-sufficient, on-prem and quantised 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 summarisation 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 and 3x Distinction Certificate Awardee for academic excellence at PES University.

• 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 the Big Data course at the Department of Computer Science, PES University.
• Setup automated grading of submissions, designed and graded coursework, assignments and project deliverables, and delivered hands-on sessions on Hadoop and Spark for a class of 600+ enrolled students.