Senior engineer with 15+ years building low-latency, mission-critical front-office trading systems at Tier-1 investment banks (RFQ/OMS/FIX across Rates/FX/Equities). I focus on latency-sensitive flows, resiliency, and production ownership—where correctness, operational discipline, and rapid incident response directly matter.

I’m now extending that same engineering mindset into LLM-powered diagnostics and agentic workflows: systems that can turn noisy production signals into structured, evidence-backed explanations and next actions—while staying auditable and safe for real environments.

I use this GitHub space as a practical lab for production-grade AI in engineering workflows—especially reliability, incident investigation, and developer productivity.

• Smart Enterprise Diagnostics (SED): Open-source experiments in LLM/agentic incident investigation — timeline reconstruction, evidence-linked summaries, hypothesis ranking, and faster triage.
• AI Tooling Workflow: Experimenting with agentic IDEs and coding agents (e.g., Antigravity, Cursor, Codex) to stress-test where they help (and where they break) in real codebases and robust Java systems.
• Verifiable Automation: Moving beyond “black box” outputs — designing workflows with traceability, explicit assumptions, and reproducible steps.

• Core: Java (expert), concurrency/multi-threading, low-latency patterns, performance tuning, production debugging.
• Trading Domain: RFQ engines, OMS/EMS, FIX, ION MarketView, front-office integrations (Rates/FX/Equities).
• AI / Reliability: LLM orchestration, agentic workflows, evaluation/guardrails, prompt+tool design for engineering systems.

I’m interested in AI that improves outcomes under production constraints: measurable speed-ups in diagnosis, fewer repeated incidents, and better operational clarity—without sacrificing reliability.

Note: These projects are personal experiments, independent of my professional employment.