Chandra Bhan

About Chandra Bhan

Seasoned data & AI professional with deep experience at Amazon, Apple, Oracle, UHG, and others, specializing in architecting high-scale Data Lakehouses, Data Lakes, and cloud-native platforms optimized for enterprise AI/ML workloads.Expert in building robust data platforms that power Generative AI and agentic workflows, while implementing ETL automation, CI/CD pipelines, and performance-tuned cloud infrastructure for large-scale operational efficiency.Hands-on leader in ML engineering, agentic system design, complex migrations, BI ecosystems, and security-hardened architectures that enable autonomous, intelligent data-driven enterprises.

Experience

1. System Architect

   Oracle

   Feb 2025 — Present · Seattle, WA, US

   Architected and delivered a large-scale WAF anomaly detection and WAF4SaaS automation platform using a layered pipeline spanning rule-based detection → ML-driven anomaly analysis → Generative AI enrichment to materially reduce false positives. Integrated telemetry from security platforms including Qualys, Nessus, and endpoint protection systems to suppress known benign and tool-generated traffic. Built behavioral baselines across applications to detect deviations in request patterns, payload structure, headers, traffic rates, and geolocation. Leveraged Generative AI to classify threat types, assign risk and confidence scores, and automatically generate investigation summaries and remediation recommendations. Implemented intelligent alerting workflows that surface GenAI-enriched, high-signal findings to security engineers, improving triage efficiency and accelerating response at scale. Designed the platform for multi-petabyte throughput, high availability, and low operational cost, strengthening security posture while enabling scalable SaaS protection. Also design WAF4SaaS clarification question workflows to capture incomplete or ambiguous inputs prior to policy enforcement or automation. Built AI-driven, agentic clarification prompts that generate structured, schema-validated JSON outputs for downstream ticketing and automation systems. Standardized workflows across ACL updates, IP/Country allow-block, rate-limiting, and anomaly exception requests. Automated request validation and normalization, significantly reducing manual analyst intervention and improving auditability. Integrated outputs with SOAR/orchestration platforms (e.g, Tines) to deliver a low-cost, highly scalable automated WAF solution

Skills

• Agile Methodologies
• Data Warehousing
• Javascript
• Oracle
• Requirements Analysis
• Java
• Microsoft Sql Server
• Sql
• Software Development
• Software Project Management
• Odi
• Testing
• Soa
• Obiee
• Java Enterprise Edition
• Databases
• Pl/Sql
• Unix
• Xml
• C++
• Perl
• Shell Scripting
• Sdlc
• Windows