Technical leader focused on bringing AI products from technology to market, with a Master’s in Machine Learning.
I drive go-to-market strategy and product–market fit for enterprise AI products while translating complex technical architectures into intuitive, user-centric solutions by bridging engineering, UX/UI, and business strategy.
Led technical product strategy for 40+ B2B startups, expanding SAP store solutions by analyzing complex data schemas and ensuring technical alignment with enterprise market gaps.
Validated large-scale AI architectures and facilitated pilot programs for high-potential SaaS solutions, reducing time-to-market through deep-dive technical and performance analyses.
Translated complex business goals into technical roadmaps and data requirements, optimizing cross-functional alignment between startup CTOs and SAP stakeholders for integrated SaaS solutions.
Optimized portfolio by designing a scouting framework to evaluate startup-market fit, ensuring alignment with SAP’s strategy.
Implemented CRM and ERP systems to standardize data flow, improve reporting accuracy, identify bottlenecks and support 15% annual growth.
Designed and deployed custom database architectures and web interfaces for SMBs, automating data collection and digital operations.
Engineered a centralized IoT ecosystem by integrating sensors and deploying micro-ML models for real-time activity classification. Built end-to-end data pipelines using custom scripts to use sensor signals into a cohesive decision-making engine, enabling predictive, hands-free automation of lighting, climate, and security.
Slyde is an early stage start-up, with the idea of bringing Uber to the private flying market. I had the opportunity to design their user experience, user flows and app user interface, before they meet investors.
Gained strong practical experience in Modeling, Deep Learning, Computer Vision and NLP.
NLP Project (Grade: 93): Designed a novel reference-free approach to machine translation evaluation for English Hebrew, using semantic and model-based signals to assess translation quality without having human references.
M.Sc. Project: Building a computer vision system on AWS automated road-damage classification, covering dataset curation, deep learning model development, performance evaluation, and scalable pipeline architecture.
Combined business fundamentals with hands-on software development and data-driven decision making.
Final Project: Android (Java) application for tracking household CO₂ emissions based on electricity consumption.
