Vetted CUDA Professionals

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”

“Aspiring founder exploring a deep-tech startup at the intersection of HPC and AI, focused on tightly integrated systems that generate high-quality simulation data for applications like drug discovery and weather prediction. They appear especially motivated by unmet market opportunities and take a pragmatic view of startup risk, prioritizing ideas with strong growth potential that can become real within five years.”

“Backend engineer who built gnocal, a ~150-line stateless Go service that turns on-chain event data into standards-compliant .ics calendar feeds consumable by Apple/Google Calendar, deployed on Fly.io. Also refactored MCTS into Monte Carlo Graph Search (Python-to-Rust) using deterministic tests and state canonicalization to handle transpositions, and implemented decentralized role-based ACLs in Gno for a smart-contract web hosting network (gno.land / All in Bits).”

“Built production LLM systems focused on reliability and safety, including a plain-English deployment tool that generates validated plans and provisions to Kubernetes while preventing unsafe actions via schema enforcement and plan/execute separation. Also created multi-LLM workflows (LangGraph) and stakeholder-friendly demos at Bosch, including a PyQt/FastAPI/CUDA app comparing SAM2 vs SAMWISE for on-device object detection with intuitive UX for business users.”

“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”

“Backend engineer with end-to-end ownership of scientific and AI-powered systems, including neuron imaging pipelines at Monell Chemical Senses Center and an LLM-based structured information extraction platform for Wharton and PSG. Stands out for turning messy, compute-heavy workflows into reliable production backends with measurable impact, including saving researchers over 50 hours per week.”