Vetted Test Automation Professionals

Rodolfo Garcia

Screened ReferencesStrong rec.

Senior QA Test Analyst specializing in game and service testing

Los Angeles, CA11y exp

“Game QA/automation tester with experience at Blizzard, Bossfight Entertainment, and Netflix, spanning manual-to-automation transitions, Selenium/C# UI automation, and CI/CD nightly reporting via TestRail. Known for an end-user-driven test strategy, strong defect isolation (including crash dumps), and cross-functional test planning that influenced multiplayer UX/design decisions.”

“Amazon engineering leader (15+ years) targeting Senior Manager/Director roles, with deep ownership of contact-center latency and reliability initiatives. Shipped a global production improvement cutting call latency 30–40% and led a complex Citrix SDK integration, including incident response and a backward-compatible rollout strategy to protect existing customers while enabling new features.”

“P&G operator who is also building and selling an AI receptionist (voice agent) SaaS for healthcare/service clinics, using EHR + calendar API compatibility to target accounts and letting the Voice AI run parts of the demo to prove value. Has already closed and deployed to two clients in the last two months, with production impact via reduced front-desk overhead and automated scheduling/FAQs, and brings a structured, scalable deployment/process mindset from global WMS rollouts.”

“Unity/C# gameplay engineer from Rec Room who owned end-to-end systems spanning downloadable avatar content delivery (Addressables + auth/CDN), build automation/CI for asset bundles, and creator tooling (Avatar Studio). Also implemented AI-driven localization and generative mesh features, and has deep multiplayer experience with Photon and Photon Quantum across VR (Meta Quest) and mobile platforms, citing a 20% retention lift from faster content delivery and customization.”

“Built a production LLM-powered data ingestion workflow at Provi, an online alcohol marketplace, to clean and match millions of distributor inventory items against a product catalog. Their experience is strongest in applying LLMs to real-world, large-scale data operations with AWS Glue, S3, batching, API integration, human review, and drift detection.”

“Operations/engineering leader from Poly who optimized a professional headset portfolio by aggressively prioritizing technical debt and consolidating HW/SW platforms (down to 2 each), accelerating time-to-market by ~6 months and enabling 3–6 month variant cycles. Also drove AI-enabled quality improvements by leveraging JIRA bug data to automate test case creation and expand coverage, while aligning engineering and product management around MVO delivery.”

Jingfei Xu

Screened

Intern/Junior Software Engineer specializing in AI/ML and cloud-based systems

Mountain View, CA0y exp

“Embedded/robotics software engineer with Hyundai Motors experience who owned an AI-driven perception validation pipeline using a Transformer-based approach to generate stable synthetic in-cabin audio for autonomy/ASR testing, cutting downstream testing time by 50%+. Has hands-on ROS integration (IMU sensor streaming, inference, control nodes), MQTT-based distributed messaging, and cloud/container deployment experience (Docker, Node/Express, AWS, CI/CD).”

“QA professional with experience at Google and Meta, focused on end-to-end functional/regression testing, disciplined bug reporting (including ADB logs), and release readiness via staging build promotion and gating on critical/high-priority issues. Emphasizes PRD-driven test planning, edge-case coverage (e.g., low connectivity/low battery), and thorough verification across build versions.”

Suparna Roy

Screened

Executive Cloud Infrastructure & SRE Leader specializing in AI-driven reliability and security

Austin, TX14y exp

“Engineering/technology leader with IBM Cloud experience leading large-scale infrastructure modernization from classic architecture to a standardized VPC/next-generation DC platform. Reports major outcomes including cutting region launch time from ~18 months to ~3 months and reducing operating costs by ~80% via automation, modular undercloud services, and platform standardization, while scaling a globally distributed org with clear service ownership and accountability.”

“Robotics software candidate with hands-on experience building controllers for an Autonomous Underwater Vehicle, including dual-PID control in Python with state-space modeling and a planned path to LQR. Developed ROS nodes for odometry-based localization, waypoint planning, and control command publishing, validated through a custom Gazebo/ROS simulation workflow with control-metric-driven testing. Also worked on F1Tenth simulation and scan-matching localization (PL-ICP), with additional cloud deployment experience using Docker/Kubernetes and CI/CD.”

“Frontend-leaning full-stack engineer who built an internal real-time operations dashboard from 0→1 using React, TypeScript, Redux Toolkit, Material UI, and Node.js integrations. Stands out for hands-on performance tuning at scale—profiling and fixing excessive re-renders, optimizing live-update UIs, and iterating post-launch with caching, pagination, and observability.”

“JavaScript/TypeScript engineer from Ridgeline who built a retry feature for failed staging-to-production promotions with pre-promotion health checks. Brings a backend-scaling mindset to runtime performance work (metrics-first bottlenecking, Big-O analysis, async/parallelism, caching) and leverages Cursor/AI tooling to ramp quickly on large codebases.”

“AWS engineering manager leading delivery for an end-to-end encrypted communications product (calling/messaging/screen sharing), including shipping read receipts with full design/engineering/QA ownership. Demonstrated strong customer-driven problem solving (offline/mission users enrollment via admin one-time codes with account allowlisting) and reliability improvements (data retention bot crash RCA, monitoring/notification, and high-volume test simulation).”

“Backend/AI engineer who built a production GPU-backed real-time inference API at Nvidia and debugged burst-induced tail latency, cutting P95 by ~29% through dynamic batching and backpressure. Also shipped an end-to-end RAG + agentic operational diagnostics assistant with strict tool controls, evidence citation, confidence gating, and strong production guardrails, plus demonstrated hands-on Postgres optimization (900ms to 40–60ms).”