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Sahil B
@sahill_bharti
AI Product Manager
Indien
Englisch, Hindi
Über mich
I am a product builder with 5+ years of experience across AI/ML and LLM systems, owning products from strategy to shipment. As a founder of two AI products, I have deep hands-on grounding in RAG and evaluation, allowing me to turn ambiguous problems into sharp product bets and measurable user outcomes.
Kompetenzen
Sahil B
offline •
Durchschnittliche Antwortzeit: 1 Stunde
Meine Dienstleistungen
Entwicklung von KI-Chatbots
I will build a rag chatbot that answers from your documents with citations
Website zu App
I will convert lovable, base44 or any website to app for ios and android
Portfolio
Arbeitserfahrung
Founder & Product Lead
advaice • Selbstständig
Jan 2025 - Present • 1 yr 10 mos
Founded Advaice and took Vikket, a ball-by-ball cricket scoring app, from problem to launch on iOS, Android and web from one React and Capacitor codebase that loads at about 96 KB on low-end phones. I ran the competitive teardown of the category leaders, set a privacy-first stance with no ads and no login, specified an event-sourced scoring engine verified by automated tests, owned App Store and Play Store readiness end to end, and instrumented the acquisition-to-activation funnel in GA4.
RAG Data Scientist
Cohere • Vollzeit
Jun 2024 - Mar 2025 • 9 mos
Led a team of six with sprint-based workflows and KPIs, driving a 30% productivity increase in two quarters. Integrated RAG into the Command model to lift SQL and Python code-generation accuracy through targeted dataset curation and fine-tuning. Built scalable annotation pipelines for semantic labelling, query disambiguation and multi-hop retrieval, and stood up evaluation frameworks combining automated and human-in-the-loop metrics, raising annotation precision by 15%.
Data Scientist
Navisite • Vollzeit
Nov 2019 - Sep 2022 • 2 yrs 10 mos
Shipped an AI-driven customer support chatbot that improved engagement and self-service. Built and deployed a support-ticket prioritisation classifier with scikit-learn, NLTK and Azure that cut average response time by 20%, deployed sentiment analysis through Azure DevOps CI/CD with 80% faster integration and testing, and applied churn-risk classification across the client base, helping reduce churn by 20%.
