Sambhav Sharma

Full Stack Engineer · Systems Architect

I design and scale high-performance systems, combining engineering, architecture, and research to drive measurable product impact.

I build distributed systems and high-scale applications with a focus on performance, reliability, and clean architecture.

At BOLD, I lead engineering efforts across system design, personalization platforms, and performance optimization - delivering systems used at scale.

I also explore system design through research and POCs, working at the intersection of engineering, product, and AI-driven systems.

I focus on building systems that scale without increasing complexity - prioritizing performance, reliability, and clear architecture over quick fixes.

Experience

Module Lead (Full Stack) | BOLD
2022 — Present
Leading system architecture and personalization platforms for high-scale user applications
  • Built real-time personalization systems driving +22% user retention
  • Reduced load times by 35% by migrating to optimized PWA architecture
  • Designed event-driven systems using Kafka and Redis for analytics
  • Developed reusable design systems → 25% faster development cycles
  • Maintained 99.9% uptime through scalable cloud infrastructure
Lead (Full Stack) | CareerDose
2019 — 2022

Built and scaled multi-platform applications across web, mobile, and desktop

  • Built cross-platform apps using React, React Native, and Electron
  • Improved performance by 45%, significantly increasing user engagement
  • Designed scalable CMS with role-based access and real-time updates
  • Led frontend architecture and UX improvements across products
  • Managed and mentored a team of 8–12 engineers

Systems & Architecture

  • Designed microservices for high-scale applications using Kafka, Redis, and containerized infrastructure
  • Built real-time data pipelines powering analytics and personalization
  • Led POCs for AI-driven experimentation and recommendation systems
  • Exploring LLM-based systems for product intelligence and automation
  • Defined reusable architectural patterns across teams

Research

  • Studied how resume tools perform across ATS systems and design platforms
  • Analyzed global user behavior and tool preferences
  • Built evaluation pipelines to measure ATS parsing performance
  • Identified trade-offs between system trust, design control, and outcomes
  • Proposed AI-driven systems (LLMs, explainable scoring, dual-mode editing)
Read paper

Case Study

AI Personalization System · BOLD

Improving user engagement through real-time personalization

Problem

Low user engagement and retention due to generic content delivery across high-traffic platforms.

Approach

Designed a real-time personalization system using event-driven architecture to dynamically adapt user experiences based on behavior.

Architecture

  • Event ingestion via Kafka for user activity streams
  • Real-time processing and caching using Redis
  • Personalization logic layer for dynamic content decisions
  • A/B experimentation framework for continuous optimization

Trade-offs

  • Chose eventual consistency over strict consistency for scalability
  • Balanced latency vs accuracy in real-time recommendations
  • Optimized for system throughput over heavy computation models

Impact

  • +22% increase in user retention
  • +30% increase in feature engagement
  • Improved responsiveness of personalization systems