Suraj Thangellapally

About

Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.

Key Strengths

• Built a Python LLM-based AI code review tool with diff chunking to preserve context and avoid token overload
• Strong Kubernetes deployment fundamentals (Deployments/Services/Ingress, ConfigMaps/Secrets, replicas, rolling updates, health probes)
• Applies GitOps practices with Git as source of truth for reproducible deployments and rollback
• Designs streaming/event-style processing with focus on reliability (retries, avoiding duplicate work), throughput, and observability
• Emphasis on modular/atomic component design to improve debugging and iteration
• Bridges customers and engineering with clear stakeholder-specific communication
• Systematic security triage and reproduction of issues in controlled test environments
• Strong web/AppSec fundamentals demonstrated via XSS investigation and remediation guidance
• Methodical integration troubleshooting using multiple signals (DevTools, headers, logs) to isolate root cause
• Creates clear, repeatable documentation that accelerates resolution and reduces recurrence
• Structured approach to running multiple concurrent onboardings with milestones, risk tracking, and proactive updates
• Applies least-privilege and secure-by-design thinking for AWS agent/scanner integrations (IAM + VPC patterns)

Experience

Education

Awards

• Engineering Design Competition Winner - Dare 2B Digital (Tesla Motors)
• Dean's Honor List - University of California, Irvine

Skills

Languages