Vetted System Design Professionals

“Full-stack TypeScript engineer who has owned a real-time workflow/communication platform end-to-end in production (Node/TS + React, Postgres/Redis, Kafka, Docker/CI/CD). Demonstrates strong distributed-systems pragmatism—designing for failure with retries, DLQs, idempotency keys, and atomic writes—plus operational practices like structured logging, monitoring, and zero-downtime deployments.”

Jose Bravo

Screened

Junior Systems Designer specializing in Unreal Engine 5 gameplay & AI systems

Conroe, TX5y exp

“UE5 gameplay/systems designer focused on Blueprint-driven, shipping-quality progression and AI systems. Built and shipped a wave/difficulty progression backbone for a top-down survival game (HUD + SaveGame + robust state/edge-case handling) and optimized a multi-enemy "Weeping Angel" puppet AI in a horror project by replacing heavy Tick logic with timer-based, centralized observation checks for scalable performance.”

Omar Rahmoun

Screened

Senior Software Engineer specializing in distributed systems and AI platforms

Chicago, IL5y exp

“Senior engineer transitioning into a lead engineer role who is actively overseeing 5 developers and championing an AI-first development culture. Stands out for a highly structured approach to AI-assisted software delivery, including context engineering, phased planning, multi-agent orchestration, and deliberate hallucination mitigation rather than 'vibe coding.'”

“Full-stack product engineer who has shipped customer-facing features end-to-end, including a product detail page backed by Java/Spring Boot microservices and a React/TypeScript UI. Demonstrated measurable impact through performance and maintainability improvements (30% faster APIs, 25% less duplicated UI code, 40% reduced API complexity via GraphQL) and has operated/scaled apps on AWS with CI/CD, monitoring, and incident-driven scaling fixes.”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”

“Backend/AI engineer who has built and operated production Node.js/Express services on AWS (Postgres/Redis) and has hands-on experience shipping an AI-powered support agent using RAG (Pinecone + LLM) with grounding checks and evaluation for hallucination rate. Demonstrates strong production reliability/performance debugging, including reducing peak latency from ~2s back to sub-300ms through query and caching optimizations, plus designing agent workflows with retries and human-in-the-loop escalation.”