I work on observability engineering for production-scale distributed systems, helping teams understand system behavior through metrics, logs, and traces. My focus is on improving reliability, visibility, and cost-efficiency of observability platforms.

Alongside my professional work, I’m deeply interested in AI forensics and digital investigation — exploring how intelligent systems fail, how telemetry can act as evidence, and how forensic thinking can improve trust and accountability in modern software.

• Scaled Observability SDK adoption across 100+ applications, enabling structured logging, metrics, and distributed tracing
• Deployed Jaeger in production, driving ** observability cost reduction**
• Migrated observability platforms (Honeycomb → Jaeger, Datadog → Prometheus + Grafana)
• Built observability scorecards to track adoption and maturity across services
• Developed parity audit tools to validate migrations and enable data-driven cost optimization
• Designed dashboards and alerts for BOSH storage workloads
• Supported production incidents, tool upgrades, and observability governance

• Built a diffusion-based text-to-3D generation pipeline using PyTorch and NLP
• Improved model fidelity by 46% compared to baseline approaches
• Reduced training time by 43% through NeRF optimizations
• Enhanced semantic accuracy using LLMs (LLaMA 2) for complex prompt understanding

• Metrics, Logs, Traces: Prometheus, Grafana, Thanos, OpenTelemetry, Jaeger
• Production Observability: Distributed tracing, alerting, dashboards, telemetry cost optimization
• Platform Migrations: Honeycomb → Jaeger, Datadog → Prometheus + Grafana
• Governance: Observability scorecards, standards, parity audits

• Container Orchestration: Kubernetes
• Cloud & Infra: AWS, BOSH
• CI/CD & GitOps: ArgoCD, Helm, CI pipelines, SonarQube
• Automation: Python, Bash

• Backend: Java (Spring Boot), Python
• Event-Driven Systems: Kafka (AWS MSK), consumer lag analysis, offset management
• Quality Engineering: Unit, integration, and component testing
• Design Focus: Reliability, scalability, failure-driven system design

• Digital Forensics Tools: Autopsy, FTK Imager, EnCase, X-Ways, Zimmerman tools
• Foundations: Evidence handling, threat analysis, forensic workflows
• Exploration Areas: AI system failures, telemetry as forensic evidence, accountability in AI systems

• Languages: Python, Java, C++, Go
• Systems: Linux (CSI Linux, Kali), Windows
• Version Control: Git, GitHub

I’m also a screenwriter, interested in storytelling around intent, conflict, and truth — a perspective that naturally complements investigative and forensic thinking.

Understanding systems by studying how they fail.