smalivanov

- Develop E2E and backend automation for Polkadot-based app covering multisig, governance, and transaction flows. - Validate blockchain transaction integrity: signing flows, nonce handling, fee logic, and failure edge case scenarios. - Test account abstraction models including proxy accounts, multisig wallets, and Complex Account Structure (CAS) flows. - Detect state sync issues between blockchain, backend services, and client, including race conditions and stale state. - Identified and validated critical failure scenarios in multisig and transaction flows, preventing production-level issues. - Validate authorization logic and access control rules to prevent privilege escalation and incorrect execution scenarios. - Analyze system behavior across RPC services, caching layers, cold starts, and network latency impact on UX flows. - Validated transaction failure scenarios including invalid signatures, insufficient balance, and partial approvals. - Applied AI-driven tooling to automate test design, streamline QA workflows, and reduce manual effort across testing processes. - Contribute to release quality gates: regression validation, transaction verification, and production risk mitigation.

- Led QA team of 10 engineers and built QA processes and automation framework from scratch for microservices platform. - Integrated API, DB, and UI tests into CI/CD pipelines, reducing regression cycle time by 50% across environments. - Validated event-driven architecture using Kafka and RabbitMQ, ensuring data consistency across distributed services. - Introduced parallel test execution via Selenoid and optimized CI pipelines for faster feedback and delivery cycles. - Acted as release manager: managed vSphere environments, release stabilization, and coordinated hotfix delivery. - Performed performance and load testing using JMeter, validating system behavior and stability under high load.

- Built scalable automation framework from scratch, achieving 90% coverage across multiple web and mobile platforms. - Defined QA strategy and KPIs, reducing post-release defects by 20% and improving overall product quality metrics. - Integrated API and DB testing into CI/CD pipelines (GitLab, Jenkins), accelerating release cycles and delivery speed. - Mentored QA engineers and transitioned team from manual testing to scalable automation practices and standards. - Developed AI-based test generation tool using ChatGPT, significantly reducing manual test design effort and improving coverage.

- Performed manual and automated testing for web applications using Selenium. - Improved QA processes, increasing team throughput and defect detection rates.