Palo Alto, California, United States
(At Our Best Award - Achieve Our Best Recognition at VMware)
➢ Built a lightweight production forecasting engine for infrastructure optimization, supporting batch fitting and on-demand inference as a cost-efficient replacement for a legacy serving stack; drove improvements across dataset preprocessing, model efficiency, and core engine modules, improving runtime efficiency by ~37% and reducing customer cost by ~28%.
➢ Developed an end-to-end recommendation service that combines forecasting signals and infrastructure telemetry to identify optimization opportunities, estimate per-entity CPU/memory/cost impact, and aggregate results across large-scale environments via MapReduce-style processing for UI and REST APIs, reducing manual triage effort by ~80%.
➢ Productionized forecasting and recommendation workflows through platform migration and integration with a distributed scheduling service, enabling configurable recurring jobs with policy rules, scoped execution, exclusion controls, and execution- status tracking at scale.
➢ Built an asynchronous export pipeline for user-customized forecasting and optimization results, using configuration-driven transformations and incremental serialization to materialize large-scale outputs into downloadable artifacts for persistence, sharing, and offline analysis.
➢ Built MCP-based tooling and workflow orchestration for an internal AI agent prototype, exposing schema-driven tools across forecasting, recommendation, and scheduling services; designed LLM routing prompts and structured outputs to support reliable multi-step execution and actionable assistance.
