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Pavithran Gnanasekaran

Entry-level Software Engineer specializing in AI/ML and full-stack systems

Alvora AIUniversity at BuffaloDover, DE1 Years ExperienceEntry LevelWorks On-Site

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About

Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.

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Key Strengths

  • Shipped MVPs quickly and iterated via frequent incremental releases based on direct user feedback
  • Strong data integrity focus (backend validation + DB constraints like PK/UK/FK/NOT NULL, correct monetary types)
  • Designed maintainable React/TypeScript front ends with feature-based organization and strong typing to reduce regressions
  • Improved performance by pushing complex filtering/sorting into optimized database queries behind stable REST APIs
  • Microservices reliability at scale (handled message ordering/duplication with idempotency, durable queues, acknowledgments)
  • Implemented centralized logging with correlation IDs to speed cross-service debugging
  • Built internal HR data validation/reporting dashboard with measurable impact (50%+ more issues found; 70% less correction time)

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Experience

Software EngineerAlvora AI · Aug 2025 – Present
Research AssistantUniversity at Buffalo · May 2025 – Presentpart-time
Software Engineer- Data & AI PlatformsUniversity at Buffalo · Mar 2025 – May 2025
Software Developer Intern – Artificial IntelligenceAlvora AI · Aug 2025 – Dec 2025internship

Education

University at Buffalomaster, Computer Science – AI & ML (2025)

Languages

English

Publications

1 publication

Medical AISelf-supervised learningMRI representation learningBrain disorder classification

Memberships

  • IEEE

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Pavithran GnanasekaranEntry-level Software Engineer specializing in AI/ML and full-stack systems