Vetted Education Professionals in the DMV

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“Former Division III college football coach with extensive experience supporting collegiate and professional athletes, including work with Under Armour athletes (e.g., Kelsey Plum, Steph Curry) and Baltimore Ravens player engagement. While not hockey-specific, they emphasize end-to-end athlete guidance (goal-setting, transparency, family support) plus social media/profile-building to increase athlete visibility.”

“Junior engineer who built a novel, highly-performant database explorer at Electric Capital: a TypeScript/React UI backed by Zig-compiled WebAssembly that loads data locally for fast filtering/sorting and responsive lazy loading, with CI/CD and Cloudflare-based auth. Also led graphics work on a WebGPU browser game engine capstone, creating TypeScript abstractions over WGSL/shader and GPU buffer complexity to accelerate teammates.”

“Robotics software engineer with experience spanning safety-critical embedded medical hardware (low-cost neonatal baby warmer with PID temperature regulation) and advanced multi-robot planning research (belief-space planning with abstraction + MCTS to handle uncertainty). Strong ROS/ROS2 practitioner (Nav2/SLAM Toolbox/MoveIt) who builds custom packages (e.g., Insta360 panoramic imaging) and is hands-on debugging real robots from SLAM/frontier exploration to multi-robot collision avoidance and real-time performance.”

“LLM/AI agent engineer with consulting/contract experience (Kanhaiya Consulting LLC) who deployed a production AI agent to automate BIM list workflows end-to-end—from database understanding and data cleaning to automated visualizations/dashboards. Worked around restricted real-time data access by generating synthetic data and improving outputs via supervised fine-tuning, and uses AWS-based LLMOps observability (Opic/OPEC) plus hybrid retrieval (vector+BM25 with reranking) to optimize relevance, latency, and cost.”