Vetted Teaching Assistants in Texas

“Robotics software candidate whose thesis focused on multi-robot warehouse coordination using MAPPO reinforcement learning, trained in simulation (LBF environment, Isaac Sim/RViz) and deployed onto three real-time robots. Built custom ROS 2 Humble nodes for multi-robot control with namespaces, TF broadcasting, and an RL pipeline integrating LiDAR odometry and camera observations.”

“Customer-facing platform support professional focused on application security and reliability for SaaS integrations, with hands-on experience troubleshooting API auth failures and data update issues using Linux/app/DB logs, SQL, and monitoring. Demonstrates strong security fundamentals (least privilege, TLS/access controls, credential rotation) and can design secure AWS agent integrations while also supporting Azure containerized REST API deployments via CI/CD.”

“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”

“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”

“Built and productionized a MediCloud/Medicoud LLM microservice platform that lets clinicians query medical data in natural language, orchestrating multi-step RAG-style workflows with LangChain and evaluating/debugging with LangSmith. Delivered measurable gains (consistency ~70%→90% / +20%; latency ~2.0s→1.1s / -40%) by implementing structured prompts, fallback logic across multiple LLMs, hybrid retrieval tuning, and AWS Lambda performance optimizations (package size, async, caching).”