Vetted Testing Professionals

Urvish Shah

Screened ReferencesStrong rec.

Mid-level Robotics Software & Systems Engineer specializing in ROS2 multi-robot autonomy

Buffalo, NY4y exp

“Robotics software engineer with ROS2 multi-robot experience spanning decentralized signal source localization (LoRa RSSI on TurtleBot3) and a master’s-thesis project on collaborative object transportation with 4 robots. Strong in sim-to-real debugging—implemented noise modeling (RBF) and practical hardware/coordination fixes (CoG tuning, clock sync/flags) to make algorithms work reliably on real robots.”

“Frontend engineer with strong depth in React/TypeScript map-heavy applications, including geocoded CSV workflows, Google Maps integrations, and large-scale record management tools. Stands out for diagnosing tricky production issues involving rendering race conditions, stale state, and frontend/backend contract mismatches, then refactoring toward scalable, maintainable architectures.”

“Full-stack product engineer who independently built and maintained a marketplace app end-to-end, including React/Node/PostgreSQL architecture, real-time chat, S3 uploads, and OpenAI-based listing validation. Also shipped backend-heavy workflow features for a nonprofit upward mobility platform using Supabase edge functions, with a clear focus on security, role checks, and operational reliability.”

“Robotics intern at the University of Delaware who built and debugged ROS2-based multi-robot coordination systems, focusing on real-time reliability (timestamp alignment, latency/jitter instrumentation, QoS/executor tuning). Also improved SLAM stability by fixing LiDAR/encoder synchronization and tuning state-estimation parameters, with a simulation-first workflow using Gazebo and Docker/CI for reproducible deployments.”

“AI Software Engineer (student employee) at University of Washington IT who helped deploy "Purple," a governed, explainable LLM platform on Azure used by 100,000+ students/faculty/staff. Independently led scalable reliability efforts by building automated agent quality/load/red-team testing and CI/CD health validation (Playwright/Node.js, Azure DevOps), and previously built an explainable AI scheduling assistant for clinical operations at Proliance Surgeons.”

Sudheer koki

Screened ReferencesStrong rec.

Mid-level AI/ML Engineer specializing in predictive modeling, data pipelines, and RAG systems

Florida, USA5y exp

“Built and productionized an LLM-powered internal knowledge search system in a regulated environment, using embeddings/vector DB retrieval with strict grounding and confidence gating to reduce hallucinations. Reported ~45% accuracy improvement over keyword search and implemented end-to-end orchestration, monitoring, CI/CD, and incremental re-indexing to manage latency and data freshness while driving adoption with business stakeholders.”

William Armstrong

Screened ReferencesStrong rec.

Junior Product Engineer specializing in AI and SaaS

San Francisco, CA1y exp

“Product intern at an AI startup (AdvisorGPT) who helped turn an LLM-based prototype into a production SEO blog-generation workflow that matched a firm’s tone/voice and targeted specific search phrases. Strong at bridging technical and non-technical teams, rapidly learning new AI tooling, and driving adoption through customer calls, UX improvements, and customer-facing demos/workshops.”

Carlotta Island

Screened ReferencesStrong rec.

Junior Frontend Engineer specializing in React and FinTech web applications

3y exp

“Developer who uses AI as a practical collaborator rather than a crutch, pairing tools like Claude with console logging, testing, and hands-on validation. They emphasize understanding code, data flow, and architecture while staying current by building projects and following AI and engineering communities.”

Brandon Pav

Screened ReferencesStrong rec.

Executive CTO specializing in cloud architecture, DevSecOps, and scalable SaaS platforms

Indianapolis, IN13y exp

“Founder of Pavtek Consulting LLC who acts as a hands-on technology leader for startups, building MVPs and full-stack AI products, custom ERPs, integrations, and AI agent chat tools. Notably led a major turnaround at Dyknow (EdTech), cutting major cloud waste and moving the business from a $500k annual loss to double profitability within two years before a successful acquisition.”

“Built a production AI-native internal onboarding feature that reduced manual product setup effort by combining barcode API data, product photos, structured LLM outputs, and a polished real-time camera UI. Demonstrates hands-on experience across the full stack of LLM systems: prompt/schema design, multimodal inputs, backend orchestration with SQS and vector retrieval, and production reliability through evals, telemetry, and drift monitoring.”

Timothy Flechtner

Screened ReferencesStrong rec.

Executive Technology & Product Leader specializing in games, platforms, and real-time systems

23y exp

“Engineering leader with gaming and fintech/trading-firm experience who built a sponsor-funded gaming rewards platform (AmEx-style) requiring integrations across Steam/Epic/Xbox/PlayStation and Discord. Emphasizes production-grade, 12-factor delivery with Terraform/Docker/CI/CD and automated testing, plus pragmatic org scaling and remote-team culture built on aligned autonomy and trust.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”