Vetted Scrum Professionals

“Frontend engineer focused on React and TypeScript dashboards with dense data, fast search/filter workflows, and reusable UI architecture. They stand out for improving production performance by identifying unnecessary re-renders, optimizing state and data loading, and making complex reporting interfaces easier to navigate.”

Ryan Peterson

Screened

Senior Full-Stack Engineer specializing in Python web and AI applications

New York, USA8y exp

“Backend-heavy full-stack engineer with startup fintech experience who built a real-time lending eligibility service that cut decision time from 12-24 hours to under 5 seconds and lifted conversion by ~15%. Also has B2B SaaS experience automating construction workflows, with strong hands-on work across React/TypeScript, Python/FastAPI, Node.js, Kafka, Redis, and some Go.”

Carlos Morales

Screened

Director-level QA Manager specializing in AAA game testing, live services, and VR worlds

San Francisco, CA16y exp

“Game QA professional with ~5 years in embedded QA roles across major studios (Experis Game Solutions, Activision, Ubisoft, Meta), including ownership of the Weapons feature area on Ubisoft’s XDefiant and feature testing on Warzone. Known for risk-based, cross-system test coverage (matchmaking/party, progression, entitlements/MTX, performance across PC/console, live-service integrations) and for leading cross-discipline meetings and release readiness reviews.”

“Engineering leader (player-coach) recently at Autodesk driving a major sales-motion transformation spanning account hierarchy, commissions/quotas, and downstream financial/sales forecasting impacts. Led cross-functional design with enterprise architects and shipped the end-to-end release, using POCs and Anaplan what-if modeling to validate a risky hierarchy change while coordinating delivery across India/Singapore teams and instituting structured Jira-based tech debt/support tracking and automation (dbt, GitHub Copilot).”

“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”