Denis Klymenko

Owned performance and architecture of drilling analytics dashboards used by enterprise oil & gas clients. Collaborated closely with QA, designers, and PM. Conducted code reviews and designed a 7-phase refactoring playbook for a complex charting codebase.

Impact: Cut page load from 5s to 1.5s (3x faster) by eliminating render cascades, fixing chart caching, and lazy-loading heavy modules. Restructured the codebase into a domain-driven architecture.

Sole developer of an 8-service production ecosystem across 5 countries. 3 AI systems live in prod: a 21-tool AI consultant handling bookings, FAQs, and service guidance across 4 channels, an AI multi-agent editorial pipeline (~40 articles/week: research → generation → SEO → images → quality gates → medical safety → editor review), and embedding-powered semantic search. Plus analytics, admin panel, payments, and transactional emails.

Impact: Deployed a production AI agent running at $0.015 per dialog with near-zero hallucinations. 6% organic-to-booking conversion. Thousands of active Personal Cabinet users. Built cross-domain analytics with phone-based attribution.

Led frontend modernization across 3 internal products, working with QA, backend engineers, and PM. Moved the stack from legacy setups to a unified Next.js architecture. Conducted PR reviews and improved security by integrating automated vulnerability checks into CI/CD pipelines.

Impact: Rewrote the authentication layer across 3 products, eliminating critical security vulnerabilities in session handling. Migrated 50K+ lines to TypeScript and Next.js — reduced onboarding time for new engineers and cut regression bugs through strict type safety.

Built 2 greenfield React applications from scratch for US and UK enterprise clients. Owned full frontend delivery — from architecture and component design to testing and production release.

Impact: Delivered Modo Energy analytics platform with 40+ interactive chart widgets for the UK energy storage market. Built Zymergen data visualization tool handling large biotech datasets with complex filtering and drill-down interactions.