AI-Powered Financial Alert System
Featured
Agentic AI system for financial analysts that identifies market risks and opportunities by synthesizing public, premium, and internal data sources in real-time.
Python Anthropic Claude DynamoDB S3
Built an intelligent alert system that monitors market conditions and automatically surfaces actionable insights for financial analysts.
Key capabilities:
- Real-time synthesis of multiple data streams (public markets, premium feeds, internal data)
- Agentic AI reasoning to identify risks and opportunities humans would miss
- Automated prioritization and alerting based on materiality and urgency
- Enables rapid response to changing conditions in high-velocity markets
Mainframe to Cloud Modernization
Featured
Led migration from monolithic web application and mainframe backend to cloud-native microservices architecture. Zero-downtime transition with improved scalability and reduced operational costs.
Java Spring Boot Db2 on z/OS PostgreSQL React Node.js Kafka Kubernetes
Orchestrated a complete platform modernization from legacy mainframe systems to cloud-native architecture while maintaining business continuity.
Key achievements:
- Strangler pattern migration from Db2 on z/OS to PostgreSQL with zero data loss
- Decomposed monolith into event-driven microservices using Kafka for async communication
- Containerized services deployed on Kubernetes with auto-scaling and self-healing
- Reduced infrastructure costs by 60% while improving performance and developer velocity
Executive Cyber Risk Dashboard
Featured
Real-time cyber risk monitoring platform for C-suite executives. Mobile-first iPhone app with live alerts, interactive drilldowns, and comprehensive risk analytics.
React Python AWS Glue S3 Snowflake Xcode Spark WebSockets
Built an end-to-end risk intelligence platform delivering real-time cyber threat visibility to executives on mobile and web.
Key capabilities:
- Data pipeline processing security events using Spark and AWS Glue
- Native iOS app with real-time alerts via WebSockets for instant risk notifications
- Interactive drill-down capabilities from executive summary to raw security events
- Unified data product with APIs serving both mobile and web applications
Investments Data Warehouse & AI Platform
Unified data platform aggregating investment data with tiered pipelines (real-time to daily batch), data products, APIs, and AI integration via Model Context Protocol (MCP).
Python AWS Glue Spark S3 Snowflake MCP Node.js
Built a comprehensive data infrastructure for investment analytics with intelligent pipeline orchestration and AI-native integration.
Key capabilities:
- Tiered data pipelines: near real-time for volatile market data, daily batch for static datasets
- Snowflake-based data warehouse with curated data products for different consumer needs
- RESTful APIs exposing investment metrics and portfolio analytics
- AI integration via Model Context Protocol enabling LLMs to query investment data directly
AI Employee Assistant with RAG
Retrieval-augmented generation chatbot that helps employees quickly find company policies and procedures. Reduces support tickets and improves policy compliance.
React Node.js LangChain pgvector
Built an intelligent chatbot that serves as a 24/7 knowledge assistant for employees navigating company policies and procedures.
Key capabilities:
- Vector-based semantic search using pgvector for accurate document retrieval
- Context-aware responses grounded in company documentation
- Natural language interface that understands questions in plain English
- Reduces HR/support burden by surfacing answers instantly
Want to See More?
These are just highlights. I'm always working on something new.
Get in Touch