Vetted Visual Studio Code Professionals

“Early-career/learning-stage candidate focused on LLM systems; has not yet built or deployed production AI applications but is actively learning orchestration (Microsoft Semantic Kernel) and core patterns like RAG, embeddings, and model selection based on business requirements.”

“College-trained Unity/3D technician with hands-on experience building real-time VFX and optimized 3D assets using Blender + Unity. Has contributed to student game projects across multiple disciplines (VFX, 3D modeling, animation/Animator, and music/audio) and iterates effects based on group feedback and user testing. Seeking an internship/stage role with compensation around $450/month (~$5.4k/year).”

“Frontend engineer focused on scalable React + TypeScript architecture and design-system style reusability, with demonstrated refactoring impact in legacy codebases. Delivered a high-visibility, filterable “Project Showcase” feature in 3 weeks (CMS-integrated, responsive, CI/unit-tested) and has implemented real-time UI updates via WebSockets for chat profile presence.”

“Robotics software engineer with hands-on experience integrating vision-based perception with control logic in ROS simulation environments. Focused on debugging real-time timing/data-flow issues, improving system stability through incremental scenario testing in Gazebo, and supporting reliable deployments with Docker and basic CI/CD automation.”

“Console-focused QA tester with hands-on PlayStation (PS4/PS5) functional/compliance testing experience, including TRC-driven validation, stability checks, controller input testing, and performance dip investigation in combat-heavy scenarios. Also tested a Nintendo casual puzzle-adventure title and uses AI assistants to speed up crash/log analysis and produce clearer test cases and bug reports.”

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”