Fraud · risk · payments · AI platforms

Engineering systems that turn scale into advantage.

I’m a Senior Software Engineer at Walmart Global Tech, building fraud prevention and risk platform systems for real-time decisioning. My work spans distributed backend services, event-driven pipelines, machine learning integrations, developer platforms, and applied research across Walmart, Adobe, Medtronic, and Purdue.

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Walmart Global Tech Adobe Medtronic Purdue University

About

I like working on systems where reliability, correctness, scale, and speed all matter at once.

Before Walmart, I built robot-assisted navigation software at Medtronic, worked on Adobe’s extensibility platform for hundreds of native plugins, and completed my M.S. in Computer Science at Purdue with a specialization in machine learning. I’ve also published robotics research and co-invented a U.S. patent in NLP.

M.S. Computer Science, Purdue B.Tech, IIT Roorkee IEEE RAL publication U.S. Patent 10,650,094

What I’m best at

Designing backend platforms, building event-driven architectures, integrating ML into production systems, and shipping durable engineering improvements that show up in performance, scale, and product impact.

Backend architecture Fraud & risk systems Cloud-native services ML infrastructure Platform engineering

Selected impact

A few numbers that summarize the breadth of the work.

15K
writes/sec supported in fraud-platform workflows
40%+
latency reduction from backend modernization
300
native plugins enabled via platform work at Adobe
50%
application launch-time improvement across flagship products

Experience

A mix of production engineering, research, and platform building.

Senior Software Engineer · Fraud Platform

Walmart Global Tech

Jan 2024 – Present
  • Modernized event and advisory distributed systems with Cassandra migration, Kafka-driven workflows, and GCP database migration support.
  • Scaled backend fraud workflows to 15K writes/sec while reducing latency by more than 40%.
  • Deployed fraud models and dynamic rules to production Java services using JavaScript and Python rule-execution engines.
  • Led GraalVM-based scripting modernization and drove returns-fraud and contact-risk initiatives from design to rollout.
Senior Software Engineer

Medtronic

Oct 2023 – Jan 2024
  • Designed and debugged C++ components for robot-assisted navigation systems used in spinal and cranial surgery procedures.
  • Contributed to implementation and reliability testing in a safety-critical medical-device environment.
Full-stack Engineer / Research Assistant

Purdue University

Aug 2021 – Aug 2023
  • Designed a containerized digital-forensics platform to mine disk images with tens of terabytes of data.
  • Implemented a Kafka-based analytics pipeline that reduced resource usage by 85% and made throughput configurable.
  • Built a Neo4j knowledge graph that decreased investigation time by about 30%.
  • Architected an RL policy with invertible neural networks, improving returns by 138% versus DDPG on benchmark tasks.
Software Engineer II

Adobe

Jul 2018 – Aug 2021
  • Built core C++, Objective-C, Python, .NET, and JavaScript framework components that enabled 300 native plugins.
  • Revamped 10+ core plugins and platform APIs, improving application launch time by 50% for 15M users.
  • Led development of 20+ reusable React Native components, improving feature-delivery velocity by 3x.
  • Received recognition for prototyping and resolving critical deployment bugs affecting Creative Suite delivery.

Projects, research & recognition

Highlights pulled from research, patent work, applied ML, and mentoring.

Publication

MANER

2023

Co-authored research on multi-agent neural rearrangement planning of objects in cluttered environments, published in IEEE Robotics and Automation Letters.

Patent

U.S. Patent 10,650,094

2020

Co-inventor on “Predicting Style Breaches Within Textual Content,” based on deep learning and NLP research at Adobe.

Selected ML projects

Applied experimentation

  • Multi-robot rearrangement planning with vision transformers and improved success rate.
  • Political bias detection using contextual embeddings with 79.4% prediction accuracy.
  • Bayesian Deep-Q Networks in PyTorch with stronger returns than Double DQN on Atari tasks.
  • Group recommendation on Google AI Cloud using relational graph convolution.
Leadership

Mentoring & awards

  • Led CSR education initiatives and mentored student and early-career engineering groups.
  • Received Adobe Spot Awards for technical contributions to Creative Suite deployments.
  • Won Best Project Award in Adobe’s Java and Web Technologies Boot Camp.

Skills

Production engineering, cloud, data, ML, and frontend delivery.

Backend & distributed systems

Java, Python, C, C++, C#, Scala, Spring Boot, Node.js, Flask, REST APIs, GraphQL, Kafka, Apache Spark, microservices, event-driven architecture, GraalVM.

Data, databases & cloud

GCP Compute Engine, AWS EC2, AWS Lambda, AWS EKS, AWS S3, Cassandra, PostgreSQL, MySQL, MongoDB, Neo4j, Redis, Elasticsearch, Docker, Kubernetes, Jenkins, Grafana, Prometheus.

Machine learning & research

PyTorch, TensorFlow, NumPy, Pandas, scikit-learn, OpenCV, ROS2, NLTK, reinforcement learning, fraud models, rule engines, robotics and planning research.

Frontend & product engineering

React, React Native, Angular, JavaScript, HTML, CSS, webpack, design systems, developer tooling, and cross-functional product delivery.

Education

Purdue University

M.S. in Computer Science, specialization in Machine Learning, with robotics thesis work and teaching assistant experience in statistical machine learning.

Indian Institute of Technology Roorkee

B.Tech in Electronics and Communication Engineering with a minor in Computer Science.