Architecting and leading the redesign of the end-of-day risk computation platform processing $14B+ portfolios, while building AI-assisted tooling that accelerates quantitative model migration at scale.
Redesigned the end-of-day risk computation platform processing $14B+ portfolios, introducing a model orchestrator engine with dynamic dependency resolution and intelligent workload bucketing.
Redundant model execution across portfolios caused excessive compute spend, slow processing times, and frequent SLA breaches during end-of-day risk runs.
Built a model orchestrator engine with dynamic dependency resolution and intelligent workload bucketing that eliminated redundant model execution across the platform.
Reduced compute usage by ~35%, processing latency by 30%, and SLA breaches by 80%.
Accelerated the strategic migration of 500+ quantitative risk models by architecting an AI-assisted platform that automated model transformation workflows across QR and engineering teams.
Migrating 500+ quantitative models manually required coordinated effort between QR and engineering teams, estimated at 600+ developer days.
Built an AI-assisted migration platform using LLM integration to automate model transformation workflows, reducing manual intervention across teams.
Eliminated 600+ developer days of manual effort. Enabled faster model onboarding from 10 days to 5 hours via self-service declarative risk modeling.
Established platform reliability standards across automated regression testing, execution observability, and operational runbooks for overnight risk computation.
Overnight risk computation failures were frequent, with no consistent testing, observability, or incident response patterns across the platform.
Established automated regression testing, execution observability dashboards, and standardized operational runbooks. Provided technical mentorship to 6 engineers.
Reduced overnight risk failures by ~45%. Grew engineering capability through mentoring 6 engineers via system design reviews and technical coaching.