# Hammad Shahid > Software Engineer & Forward-Deployed Engineer | AI Platforms & Reliability | Capital One | MBA in AI & Analytics @ UIUC | CS @ UMD Location: New York City Metropolitan Area, United States Profile: https://flows.cv/hammad 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. I’ve built customer-facing mobile features, internal developer platforms, and observability systems supporting high-traffic, mission-critical applications. I enjoy ambiguous problems, rapid prototyping, and turning messy requirements into production-ready systems. Recently, I’ve been focused on AI-enabled developer tooling, observability platforms, and improving how engineering teams build and operate software at scale. Interested in roles across full-stack engineering, AI platforms, developer tools, and customer-facing technical teams. ## Work Experience ### Software Engineer @ Capital One Jan 2022 – Present | 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. ### Android Engineer @ Capital One Jan 2021 – Jan 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. ### Software Engineer @ Capital One Jan 2020 – Jan 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. ### Application and Systems Developer @ Prudential Financial Jan 2019 – Jan 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. ### Software Developer Intern @ Audtra Jan 2018 – Jan 2018 | New York, NY Android engineer Intern at Audtra. I improved UI/UX of Audtra Android app to match iOS counterpart ### Engineering Aide Intern @ Con Edison Jan 2015 – Jan 2015 | New York, NY I coordinated with a group of 4 to create a database of all utility expenses for unit checks through the company's Work Management System. I also organized all data in the database with SQL, Excel, and Tableau to make a user interface for other employees to use. Attended meetings with contractors to find solutions for savings costs. Finally, I presented my findings to senior level employees during the end of the internship. ## Education ### Master of Business Administration - MBA University of Illinois Urbana-Champaign Jan 2026 – Jan 2028 ### Bachelor of Science in Computational Science University of Maryland Jan 2017 – Jan 2021 ## Contact & Social - LinkedIn: https://linkedin.com/in/shahid-hammad - Website: https://hammad.codes --- Source: https://flows.cv/hammad JSON Resume: https://flows.cv/hammad/resume.json Last updated: 2026-03-22