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.

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.

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.

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.

Android engineer Intern at Audtra. I improved UI/UX of Audtra Android app to match iOS counterpart