Resume
Highlights
- →Led delivery of a high-impact AI-enabled engineering system on a $500K+ engagement, owning end-to-end architecture and full-stack implementation in C#/.NET 8, React, and SQL Server
- →Built AI-assisted document-to-data pipelines in Python + SQL Server using Azure Document Intelligence (PDF→structured JSON) and Azure OpenAI (embeddings + hybrid RAG + GPT-4 extraction)
- →Implemented scalable batch processing with multithreading/parallelism, retry/backoff for 429s, structured logging/telemetry, and automated quality gates (golden-set validation with precision/recall/F1)
- →Delivered stakeholder-ready outputs via Excel exports and dashboards/status reporting, enabling downstream enterprise system updates and ongoing tracking
- →Shipped 100+ production changes across C#/.NET, AngularJS, SQL, and Jenkins CI/CD, improving API performance (caching/call reduction), test coverage (unit/integration + SpecFlow BDD), and pipeline reliability
Experience
Full-Stack Software Engineer
This role was unique in that it combined internal software development with external, client-facing delivery. I built and maintained internal tools while also translating stakeholder requirements into production-ready solutions, owning end-to-end implementation and communicating progress, risks, and outcomes directly with clients.
- •Led internal AI enablement by delivering the team’s first AI-enabled engineering application on a $500K+ client engagement, owning end-to-end architecture and full-stack development (.NET 8/C#, React, SQL Server) as the sole developer
- •Delivered an end-to-end data pipeline to build a baseline equipment/cable population from 579+ PDFs, reconcile against enterprise exports, and produce client-required Excel datasets plus dashboards/status reporting for downstream system updates
- •Built an AI-assisted extraction/validation system (Python, SQL Server) using Azure Document Intelligence (PDF→structured JSON) and Azure OpenAI (embeddings + hybrid RAG; GPT-4 field extraction) with post-processing rules/flagging and human-in-the-loop review workflows
- •Implemented scalable batch processing with multithreading, retry/backoff for 429s, robust error handling, and QA against a manually verified golden set (accuracy/precision/recall/F1) to improve reliability in a regulated domain
- •Built LLM-driven extraction and automation pipelines in C#, Python, and Node.js to convert unstructured documents into structured datasets used by 100+ engineers across internal teams
- •Drove internal adoption of AI developer tooling by running training sessions (Cursor workflows) and authoring step-by-step documentation for installing/using Azure MCP with Cursor for repo/PR context
Software Devloper (Co-op)
During this internship, I learned a lot about building and operating production software at scale, shipping full-stack features while also debugging real customer issues end-to-end, collaborating closely with PM/QA/support, and improving performance, test coverage, and CI reliability.
- •Delivered 100+ full-stack features and fixes across C#/.NET, AngularJS, and SQL Server, building REST APIs and UI components tracked via Jira
- •Reduced API latency by ~20% through call reduction and caching strategies, improving responsiveness in production workflows
- •Reduced backend server load by ~15% by refactoring AngularJS/RxJS request patterns and eliminating redundant calls
- •Increased automated test coverage on key .NET services from ~60% → ~80% by adding unit/integration tests and SpecFlow BDD scenarios
- •Integrated 25+ BDD scenarios into Jenkins CI/CD and improved pipeline reliability, reducing flaky failures by ~10%
- •Performed root-cause analysis on production issues, including resolving a deadlock-related incident to restore system reliability
Test Automation & Performance Engineer (Intern)
During this internship, I learned a lot about building reliable quality engineering pipelines in a production environment, expanding automated regression coverage, debugging flaky end-to-end tests with developers/QA, and using profiling/load testing to surface performance bottlenecks and drive measurable improvements.
- •Automated 50+ regression test cases using Serenity BDD, Selenium, and Java, improving confidence in high-traffic release flows
- •Increased automation coverage by ~30% and reduced manual testing effort by ~40% by expanding and stabilizing end-to-end suites for core user journeys
- •Diagnosed automation failures and performance bottlenecks, supporting resolution of 25+ high-priority defects and improving system efficiency by ~20% via profiling/load testing (Python, LoadRunner) and targeted recommendations
Project Manager (Co-op)
During this co-op, I learned a lot about driving execution across cross-functional teams, coordinating cloud migration delivery, improving delivery visibility through better tracking, and communicating technical progress and risk clearly to leadership.
- •Coordinated Azure cloud migration delivery with 30+ stakeholders, aligning technical workstreams with business priorities to support on-time execution
- •Standardized Jira/Confluence tracking and facilitated 30+ Agile ceremonies (planning, refinement, retrospectives), improving visibility and reducing blockers
- •Translated technical migration status into clear leadership updates, escalating risks early and driving timely resolution of cross-team dependencies