Resume

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Experience

Software Engineer @ Adobe
Adobe Risk Platform
San Jose, CA
Nov 2025 – Present
  • Revamped front-end SDK for Adobe’s Risk Platform; enabled collection of user and browser signals, bot detection, IP intelligence, and fraud vendor integrations across 100M+ *.adobe.com sessions weekly
  • Automated fraudulent email domain classification pipeline, expanding fraud detection coverage across 4M+ domains, $XXM ARR impact
  • Built Gardener: scheduled agents for continuous codebase health improvements. Fixed security vulnerabilities, memory leaks, thread safety issues across many codebases in Adobe ecosystem.
  • Built observability stack (Splunk, Grafana, Prometheus) and deployment pipeline with Argo Workflows; implemented canary rollouts, auto-rollback, anomaly detection for proactive incident detection.
  • Developed back-end fraud vendor integrations and risk decisioning: design decisions for parallelism, resiliency, caching, etc. Directly resulted in 400+% fraudulent payment reduction, $XXM ARR impact
Software Engineer @ Adobe
Payments & Order Processing
San Jose, CA
Aug 2024 – Nov 2025
  • Designed and built eCommerce platform features: Pause/Resume contract scheduling ($XXM ARR), daily/weekly/monthly App Store subscriptions ($XXM ARR), undo cancellation ($XXM ARR), free offer bundling ($XM ARR).
    Features spanned Adobe's Java microservices and event-driven systems (Kafka, SQS), balancing resilience and scalability for millions of daily transactions.
  • Built 1st-place hackathon-winning Commerce AI agent; enabled E2E checkout, product discovery, upsell, and Paypal-integration through a natural language chat interface.
  • Led and architected the Commerce MCP project: enabling AI-assisted request resolution, CSO remediation, and observability through a unified interface. Significantly reduced manual lookup and debug time for support and CSO incidents.
  • Led migration to Ethos Flex for Payments team: modernized CI/CD pipeline with infrastructure-as-code, canary-analysis, and built-in security guardrails.
    Maintained 4 9’s reliability across the microservice ecosystem.
Software Engineer Intern @ Adobe
Payments
San Jose, CA
May – Aug 2023
  • Designed and tested multiple ML classification models (neural nets, XGBoost, decision trees) using Spark, TensorFlow, and Elephas to drive retry cost reduction and reduce model latency by 50% via distributed PySpark RDDs
  • Built end-to-end data mining processes on payment data to optimize retry schedule with 42 actionable recommendations
  • Onboarded and documented team migration of big data querying processes from Hadoop to Apache Databricks
Consulting Project Lead @ Starbucks
Valley Consulting Group
Client: Starbucks Emerging Technologies
San Francisco, CA
Sep – Dec 2022
Data Engineer Intern @ U.S. Venture
Breakthrough
Green Bay, WI
May – Aug 2022
  • Deployed an end-to-end geospatial index for the pricing methodology team by integrating a routing REST API, enabling internal query operations to a NoSQL (GeoJSON) database built on an R-Tree data structure
  • Implemented ETL pipeline processes in Python on Google Cloud Platform, reducing pricing latency by 40%
  • Contributed to backend development in agile methodology, improving architecture of SaaS product FELIX
Consulting Data Scientist @ Roku
Valley Consulting Group
Client: Roku
Berkeley, CA
Jan – May 2022
  • Developed ML techniques (k-means, decision tree, PCA, logistic regression) to perform unsupervised customer segmentation analysis on 1M+ Roku Remote Voice users over an eight-week timespan
  • Built consumer profiles of distinct Roku Voice user groups, significantly increasing Roku Voice product penetration
Consulting Privacy Analyst @ Airbnb
Valley Consulting Group
Client: Airbnb
Berkeley, CA
Sep – Dec 2021
  • Pinpointed data privacy features to grow product penetration of Airbnb’s digital nomad demographic by 36%
  • Conducted competitor UI/UX research to heighten perception of user data management rights and portability
  • Scraped and analyzed web forums for Airbnb pain points guiding customers through the data privacy lifecycle