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Senior Machine Learning Engineer

J.P Morgan Chase & Co.

• Devised a deep-cross multimodal ranking model for offer category recommendation serving 90M Chase customers, increasing clicks by 12% in A/B tests and driving $100M in revenue; outperformed BAU by 4x and random by 2x in F1 score.
• Conceived an agentic framework to build digital twins of Chase customers for offline A/B testing our models — fine-tuned LLaMA-3 70B (using RAFT) on GPT-4 distilled tabular interaction data to hyper-personalize agents—first-authored a NeurIPS 2025 paper.
• Pioneered a distributed LLM inference and fine-tuning framework using vLLM and torchtune on team’s infrastructure, enabling 2 brand new projects, cutting setup time by 8 weeks, and establishing the team’s first GenAI environment.
• Eliminated feature errors and boilerplate by building a PySpark feature transformation library, enabling 20+ developers to define new jobs in 1 day instead of 7 using simple configs and functional primitives, reducing production model failures by 20%.

Senior Machine Learning Engineer

Morgan Stanley

• Spearheaded an 8-member team to build a distributed batch-job failure prediction model using Spark MLlib & Scala, achieved 94% accuracy, and reduced downtime by 100+ hours annually—recognized as the top contributor to global firm resiliency.
• Revolutionized graph search by creating a natural language-to-Cypher query translation framework, empowering 50+ teams' ML models.
• Researched and developed a domain-agnostic recommendation engine (using large-scale and distributed training of deep learning models) powered by graph databases, recognized as most innovative firmwide project.

Data Scientist Intern

Locale.AI

• Founding Member of the Data Science team, developed low-latency, real-time geospatial ML frameworks to power intelligent location-based decision-making.
• Invented a novel clustering algorithm for human activity discovery from raw geospatial ping data using semi-supervised representation learning, enabling the automated identification of 18% more business areas from previously untapped data.
• Productionized and scaled a high-throughput timeseries anomaly detection framework using temporal convolutional networks (TCN), increasing request processing speed 6x and empowering 10 companies to predict vehicle vandalism and trip delays with greater reliability.

Machine Learning and Web Development Intern (Part-Time)

S4S Technologies

• Designed and built the entire internal operations dashboard, streamlining team workflows and decision-making.
• Developed a computer vision solution for automated food quality assurance, enhancing accuracy and efficiency in defect detection.

Machine Learning Intern

Sigmaways Inc

• Developed an AI-driven portfolio recommendation engine using an LSTM-based predictor and a Meta-Heuristic Optimizer, optimizing personalized investment strategies.
• Built and applied NLP techniques in a seamless chatbot interface, improving user interaction and accessibility to financial analysts.

Software Engineering Intern

Nova (P & D)

• As a Summer Intern, I enhanced the bookstore's software capabilities by developing a new Edge-Billing system, enabling multiple staff members to finalize bills directly on their smartphones, which significantly increased transaction speed through simultaneous billing.
• Leveraged data analytics to introduce new software features that provided better insights into sales and purchases, further optimizing operations.
• Streamlined the transaction process by implementing a barcode scanner app linked to Google Docs, enabling staff to independently process orders, which saved months of hiring efforts and helped the bookstore meet its sales target.

Research Scientist Intern

Abacus.AI

• Led research on LLM adapter linearity (soft prompts, LoRAs), developing a gradient-based algorithm that approximates full fine-tuning performance with only (depth × num of LoRA) parameters, achieving <10% F1 error on Bloom for NewQA and SQuAD.
• Devised coding and reasoning LLM evaluation benchmark to debunk staleness in popular NLP benchmarks due to memorization in LLMs (GPT4, GPT3.5-turbo), open-sourced library, contributed to writing research paper accepted at ICLR 2024 (contributed talk). Code - https://github.com/abacusai/to-the-cutoff

Graduate Research Assistant

Robotics Institute, Carnegie Mellon University

• Led a team of 8 graduate researchers in ML research at the Biorobotics Lab to enhance the generalization and robustness of models for e-waste recycling. Focus areas included robust segmentation, out-of-distribution detection, and reinforcement learning.
• Invented a novel area-relaxation-based loss function that improved state-of-the-art segmentation on X-ray images of e-waste materials, increasing IoU by 15% and boosting small object detection accuracy by 80%.
• Developed and deployed a real-time distribution shift detection algorithm, achieving 99% accuracy in out-of-distribution detection for e-waste material classification.

Research Intern

SketchX Lab, University of Surrey

• Invented a novel active learning algorithm for fine-grained cross-modal instance-level retrieval tasks,
• Increased mean top1 accuracy by 5% over state-of-the-art technique, published a first-authored paper to a top-tier conference (BMVC).

Master of Computational Data Science

Carnegie Mellon University

• Select Courses
  • Large Language Models
  • Multimodal Machine Learning
  • Deep Reinforcement Learning
  • Introdution to Deep Learning
  • Introduction to Machine Learning (PhD)
  • Cloud Computing
  • Foundations of Computational Data Science
• Academic Services
  • Reviewer for EMNLP 2023
  • Core Committee Member, LTI Computing Insfrastructure

Bachelor of Computer Science and Engineering

Vellore Institute of Technology, Vellore

• Select Courses
  • Machine Learning
  • Artificial Intelligence
  • Image Processing
  • Neural Networks and Fuzzy Logic
  • Parallel and Distributed Computing
  • Virualization
  • Data Structures and Algorithms
  • Operating Systems
  • Databases
  • Networks and Communications
  • Calculus
  • Linear Algebra
  • Statics and Probability
  • Discrete Mathematics and Graph Theory
  • Theory of Computation and Compiler Design
• Extracurricular Experiences
  • Researcher at ACM-VIT
  • Core Committee Member, SEDS-VIT
  • Founding Member at Team Qubits
• Achievements
  • G. D. Naidu Young Scientist Award, 2020
  • Sir. M. Visvesaraya Award, 2019
  • Secured 1st position in 7 national hackathons
  • Ranked in Top 3 teams at 3 national hackathons
  • Secured 2 patents

AI on the Beach

• Developed a high-performance library for animating millions of geospatial pings using Dask and DataShader, enabling rapid visualization.
• Analyzed shark movement patterns with geospatial intelligence and machine learning, identifying potential attack sites in advance.

• G. D. Naidu Young Scientist Award

• Sir. M. Visvesaraya Award