Victor Santos

– Designed and built a custom multi-agent system with a writer/reviewer closed loop to author and validate structured QA prompts, using strict source-grounding to prevent hallucination, separation-of-duties review, and an automated similarity/validation gate. – Built an embedding-based semantic quality pipeline for prompt-dataset evaluation, detecting duplicate and low-diversity samples before delivery. – Developed and improved model-training processes, including new features and fixes for production ML workflows.

– Developed structured training data and reasoning workflows for SWE Agents specialized in software debugging and development. – Built a real-time ML pipeline combining Random Forest prediction with Heavy-Light Decomposition for city traffic analytics (proof of concept). – Built a multi-band real-time audio-processing case study using a hybrid segment / binary-tree data structure. – Produced game development, physics simulations, and audio processing assets for RLHF model training. – Technical leadership and Pod management: task review, performance tracking, and mentoring of new trainers; automation via web scraping, Bash, and CLI tooling. – Results: Promoted to Squad Lead within 3 months for high performance; led the 2nd best-performing squad in the team and trained several new trainers, directly driving team growth.

– Designed and implemented automated test pipelines for FastAPI services, database integrity, and RBAC using Python, SQLAlchemy, and Docker. – Optimized backend performance and error handling (recursion protection, circular-reference detection); ran PR reviews, bug-lifecycle management, and production-fix validation. – Results: Reduced response time from 7+ seconds to < 0.1s for circular-reference detection, and contributed to one-shot delivery quality that minimized rework cycles.