I’m a software engineer at Capital One working across full-stack development, cloud infrastructure, and reliability engineering. My work sits at the intersection of product, platform, and AI-assisted engineering.

2022 — NowCapital OneSoftware Engineer

2022 — Now

New York, NY

I build and scale internal AI-enabled platforms and distributed systems supporting enterprise banking applications.

I led observability and reliability rollout initiatives during Capital One’s Discover Financial migration, implementing APM-level tracing, defining SLIs/SLOs, and expanding telemetry coverage across migration-critical distributed services supporting millions of customers and billions in daily transactions. This work reduced migration-phase incident volatility by ~30% and improved mean time to detection (MTTD) by 25% during high-risk deployment windows.

In parallel, I design and develop internal platforms that centralize observability, reliability intelligence, and engineering insights:

Built backend services (Node.js, TypeScript, AWS Fargate, PostgreSQL) to ingest and normalize telemetry from CloudWatch, Splunk, and New Relic.

Developed full-stack internal tooling (React, Redux) enabling domain-level health dashboards, SEV trend analysis, and reliability scoring across banking applications.

Reduced false-positive PagerDuty alerts by 50% by designing a context-aware alert suppression engine, saving ~90+ engineering hours per month.

Increased observability coverage by 25%+ through cross-platform telemetry integration and automated monitoring workflows.

Optimized Splunk ingestion pipelines using AWS SQS and Lambda to improve cost efficiency and data retrieval performance.

Reduced SEV incidents by 35% across supported teams through reliability audits, structured SLO frameworks, and embedded observability best practices.

Leveraged AI-assisted engineering tools (Claude Code, Cursor, Copilot) to accelerate internal platform development and debugging.

My work sits at the intersection of full-stack engineering, distributed systems, and AI-driven internal platforms—building scalable systems that improve how engineering teams monitor, analyze, and ship software at enterprise scale.

2021 — 2022Capital OneAndroid Engineer

2021 — 2022

New York, NY

On the Eno team, I contributed to Capital One’s AI-powered virtual assistant called Eno, used by millions of customers, building and shipping customer-facing Android features in a regulated financial environment.

My work focused on delivering high-quality, bi-weekly releases while integrating AI-driven fraud detection and accessibility enhancements into mobile workflows.

Key contributions:

Implemented and maintained new Android features using Kotlin, Jetpack Compose, XML, and modern Android architecture patterns.

Led development of Eno Search and Eno Translate, collaborating closely with product managers, Android SMEs, and iOS engineers to ensure cross-platform consistency and performance.

Integrated Eno’s AI capabilities into fraud detection reporting workflows, improving detection accuracy and enhancing customer accessibility.

Modernized the Android frontend using Jetpack Compose and design alignment through Figma, improving maintainability and development velocity.

Proactively monitored and resolved production issues through regression testing, Firebase alerts, and PagerDuty incident workflows.

Participated in incident scenario testing and on-call rotations, ensuring stability of customer-facing banking services.

Partnered with backend, NLP, and platform teams to align mobile feature delivery with AI model capabilities and backend service performance.

This role strengthened my experience in customer-facing AI applications, cross-functional collaboration, production-grade mobile engineering, and shipping features at scale within a high-traffic financial ecosystem.

2020 — 2021Capital OneSoftware Engineer

2020 — 2021

New York, NY

Contributed to backend fraud detection systems supporting high-volume banking services, integrating AI-driven model insights into real-time transaction monitoring and reporting workflows.

Collaborated with fraud data science teams to integrate model outputs into distributed banking services, enabling real-time fraud risk scoring and alert generation.

Improved fraud detection pipeline observability by implementing telemetry, logging, and alerting across critical transaction services.

Enhanced fraud alert precision by refining monitoring thresholds and reducing redundant alert noise by ~20%, improving signal quality for fraud operations teams.

Supported production reliability for fraud-critical services during on-call rotations, maintaining high availability across systems processing millions of transactions daily.

Participated in incident scenario testing and resilience planning for fraud-related service disruptions in a regulated financial environment.

This work strengthened my experience operating at the intersection of distributed systems, AI model integration, and high-availability backend infrastructure.

2019 — 2019Prudential FinancialApplication and Systems Developer

2019 — 2019

Greater New York City Area

Migrated data from an IBM DB2 database on prem to a Postgresql database on AWS cloud with sed scripts to format differences between data. Used ReactJS & NodeJS to create a web application hosted on an AWS EC2 instance to access the Postgresql data through JSON libraries. Worked with data engineering team to create a create a second web application with PHP to access remaining IBM DB2 data. Learned and tested graph data with Neo4j & collaborated with co-interns to pitch a presentation on the use cases of graph databases such as AWS Neptune and Neo4j.