We build production AI systems and open-source desktop applications. From fractional Head of AI services to shipping LLM, RAG, and agentic systems—plus native macOS apps for voice cloning, quantum simulation, and research tools.
macOS & Open Source
Cloning & Synthesis
Strategy & Delivery
Technologies & Expertise
On-Demand Head of AI and Chief Data Scientist with 20+ years shipping production AI systems.
AIMO-2 Gold Medalist, published author (Deep Learning Interviews), and founder of QNeura.ai. With 20 years of hands-on AI and systems engineering across Fortune 500 programs and award-winning open source.
Leads strategy and delivery for LLM-powered systems, RAG pipelines, agentic AI, and MLOps at production scale—shipping working prototypes fast and scaling them into robust, observable systems.
Stack: Python/PyTorch, C++/CUDA, TensorRT, vLLM, and AWS/GCP.
Academic background: Strategic Studies (MSU), Quantum Physics (Johns Hopkins), Signal Processing (Queen Mary), Engineering (Ben-Gurion).
Written by Shlomo Kashani, this is an essential guide for aspiring data scientists and AI engineers, with clear step-by-step solutions across core machine learning and deep learning topics.
From strategy and architecture to hands-on delivery. I help post-seed startups build production AI systems that actually work.
Define your AI roadmap, evaluate build-vs-buy decisions, and architect systems that scale with your business.
Production-grade retrieval-augmented generation pipelines with proper evaluation, guardrails, and observability.
Tool-integrated reasoning agents that perform real tasks. Multi-step workflows with proper error handling.
Deployment pipelines, model serving, monitoring, and the infrastructure to run AI at scale.
Deep expertise across the full AI stack, from research to deployment.
LoRA, QLoRA, full fine-tuning with proper evaluation frameworks and A/B testing.
Dense + sparse retrieval, reranking, query expansion, and contextual compression.
OpenAI, Claude, Gemini, and local models via Ollama. Unified interfaces with fallbacks.
TensorRT, vLLM, quantization, batching, and GPU optimization for throughput.
LLM-as-judge, semantic similarity, guardrails, content filtering, and bias detection.
LangSmith, Phoenix, custom dashboards. Full tracing from prompt to response.
Reach out to discuss your AI roadmap, evaluate vendors, or get hands-on help shipping production systems.
From strategy to production. AI systems that work, not just demos.