Vivek Bhandari

About Vivek Bhandari

Results-driven technology leader with 15 years of experience in software development, enterprise architecture, and global team leadership. Expert in Java/J2EE, Spring Boot, Python, React, Angular, LLMs, Vector Databases, LangChain, and Elasticsearch, with a proven track record of delivering full-stack applications that seamlessly integrate frontend, backend, cloud infrastructure, data analytics, and CI/CD pipelines.AWS Certified Solutions Architect, skilled at driving innovation, executing complex projects under tight deadlines, and guiding distributed teams to deliver business-critical solutions. Adept at engineering intelligent systems that transform data into actionable insights, leveraging advanced AI/ML and search technologies. Experienced with enterprise reporting and visualization tools including Tableau, Qlik Sense, and QlikView.

Experience

1. Senior Vice President of Technology

   JPMorganChase

   Aug 2017 — Present

   Asset Management Data Projects• IBOR Trading Data Platform: Directed delivery of trading technology solutions for money market funds, fixed income accounts, and J.P. Morgan Securities platforms, building data pipelines to automate trade origination, syndication, allocation, and post-trade workflows.• Designed and deployed real-time data ingestion frameworks integrating market data feeds and risk engines, ensuring low-latency processing and data quality across front-to-back trading systems.• Architected data lake and warehouse integrations (AWS S3, Aurora PostgreSQL, Redshift) to unify reference data, trade history, market prices, and client metadata, supporting downstream AI/ML models and analytics dashboards.• Implemented event-driven microservices and Kafka pipelines to capture streaming trade events, improving data lineage, auditability, and compliance reporting.• Partnered with traders, and portfolio managers to design data models for exposure analysis, yield optimization, and P&L calculators, improving decision-making accuracy by 25–30%.• Applied Cache-Augmented Generation (CAG) on top of trading data pipelines, enabling portfolio managers to retrieve cached investment insights and historical analytics with sub-second latency while reducing LLM token costs.AI, Generative AI (Global Technology)• RAG + CAG Hybrid Framework: Designed a Retrieval- and Cache-Augmented Generation framework using LangChain, vector databases, and Elasticsearch on top of structured and unstructured datasets, improving search precision and response latency for advisors and portfolio managers by 40%+.• AI Marketplace & Agentic AI: Spearheaded the creation of an AI marketplace leveraging ChatGPT (GPT-4 Omni) and agentic AI agents connected to data warehouses, CRM, and portfolio systems, automating workflows and scaling data-driven decision-making across wealth management.• Enterprise Data Intelligence Platform: Consolidated structured trading data and unstructured research docum

Education

• Rajasthan Technical University

  Bachelor of Engineering (B.E.), Computer Engineering

  2007 — 2011

• Rajasthan Technical University

  Bachelor of Technology, Computational Science

Skills

• Html5
• Html
• Eclipse
• Spring
• Sql
• Javascript
• Microsoft Sql Server
• Jsp
• Ext Js
• Mysql
• Css
• Angularjs
• Java Enterprise Edition
• Oracle
• Core Java
• Java