Vetted Semantic Search Professionals

Vatsal Gabani

Screened ReferencesStrong rec.

Mid Software Engineer specializing in backend systems and AI-enabled platforms

California, USA4y exp

“Full-stack engineer with hands-on ownership of a support ticket intelligence platform, spanning React/TypeScript frontend work and backend API, PostgreSQL, Redis, and Docker-based deployment. They stand out for driving practical architecture and performance improvements in production, including moving heavy processing async and cutting response times from about 300ms to 150ms while improving reliability.”

Jeremiah Medina

Screened

Senior AI/ML Engineer specializing in LLMs, RAG, and cloud-native MLOps

Orlando, FL11y exp

“Built and owned a real-time clinical AI assistant at Andor Health, taking it from prototype through deployment, monitoring, and iterative improvement. Brings strong healthcare-focused GenAI experience across RAG, hybrid retrieval, LoRA fine-tuning, and production Python services, with measurable gains in accuracy, speed, and reliability.”

“Engineer with experience at Reddit working on high-scale backend and infrastructure problems, including API redesign for products serving 150M+ daily active users. They also built a production AI agent for automated bug triage with 97% accuracy and substantial time savings, and have hands-on full-stack/AI side-project experience using React, TypeScript, Supabase, and LLMs.”

“AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.”

Patrick Jose

Screened

Senior AI Engineer specializing in LLM applications and full-stack systems

San Francisco, CA8y exp

“Built and owned a production LLM/RAG customer support assistant end-to-end, from prototype through deployment, monitoring, and iteration. Their work automated roughly 40% of common support queries and cut response times by about 30%, while also creating reusable Python inference services that improved consistency and team velocity.”

“Atlassian ML/AI engineer who has shipped end-to-end production systems combining classical ML, streaming infrastructure, and LLM-based personalization to improve onboarding and free-to-paid conversion. Particularly strong in turning research-style RAG and reranking ideas into low-latency, reliable product systems with robust evaluation, safety guardrails, and reusable platform services for other teams.”

Ami Foelz

Screened

Principal Product Leader specializing in AI personalization, search, and e-commerce

Remote, US18y exp

“Product leader with experience building product organizations from scratch at both Amazon and LTK, with a strong focus on AI-powered search, discovery, personalization, and monetization. They combine strategic product leadership with hands-on UX and technical fluency, and have delivered measurable business impact including a 17% conversion lift and 21% revenue growth at LTK.”

Timothy Lee

Screened

Senior Full-Stack Engineer specializing in AI platforms and scalable web systems

Live Oak, FL12y exp

“Built and shipped production agentic/LLM systems that could safely perform real customer and subscription operations, not just answer questions. Demonstrates unusually strong depth in agent orchestration, tool safety, evals, tracing, and backend workflow design across Node.js/TypeScript, Go, Redis, Postgres, Kafka, and GPT-4.”

“Product leader with 8+ years across AI, platform, data, and customer-facing products, including personalization, discovery, loyalty, digital ordering, and conversational AI. Notable wins include launching an AI recommendation engine for Baskin Robbins that lifted conversion 20% and AOV 12%, and improving Apple discovery feature adoption by 25% through phased delivery under complex data-quality constraints.”