Build systems that move fast — and stay trustworthy.

Problem: Teams needed faster decisions across complex workflows where information lived in over 10 million documents, dashboards, and policies — with strict privacy, role-based access, and audit requirements.

Approach: Designed a production LLM system with RAG (hybrid retrieval + vector search), deterministic tools, and policy-aware routing. Built an evaluation harness (golden sets + regression tests), plus observability for quality/latency/cost and defenses against prompt injection + sensitive-data leakage.

Outcome: Delivered trustworthy, citation-backed answers and automated drafts for high-leverage workflows — reducing time-to-decision while maintaining controlled access, traceability, and measurable quality.

Problem: A large-scale retail product required real-time personalization signals that combined immediate behavioral events with longer-term history — while keeping metrics consistent and pipelines reliable.

Approach: Built streaming + batch feature pipelines that blend real-time events with 30/60/90-day trailing aggregates (propensity, affinity, recency/frequency). Implemented feature validation, backfills, and monitoring to keep signals stable and explainable.

Outcome: Improved relevance and consistency of downstream personalization while providing a maintainable feature foundation teams could extend safely.

Problem: Disconnected identity, product, and behavioral data made it hard to deliver consistent experiences across channels and teams.

Approach: Implemented a customer data hub with standardized schemas, identity resolution, and activation-ready interfaces. Designed data contracts, lineage, and reliability checks to keep personalization inputs trustworthy.

Outcome: Enabled consistent segmentation and personalization across products with shared definitions and repeatable pipelines.

Problem: Core APIs needed to scale predictably under heavy load while meeting strict p99 latency SLOs and reliability targets.

Approach: Optimized request paths end-to-end (caching, async patterns, connection tuning), introduced load testing + error budgets, and added observability for p50/p95/p99, saturation, and tail latency.

Outcome: Sustained high throughput with stable latency under peak traffic and clearer operational ownership.

Problem: As data volume grew to PB-scale, teams needed consistent governance, privacy controls, and auditability without blocking delivery.

Approach: Defined access-control patterns, data classification, and retention policies. Implemented lineage + cataloging, automated policy checks, and repeatable onboarding for new datasets.

Outcome: Improved compliance posture and reduced risk while keeping analytics and ML teams moving fast.

Problem: Event pipelines required high durability and scale, while enabling downstream analytics and real-time triggers without operational overload.

Approach: Designed an ingestion architecture with backpressure, retries, schema evolution, and robust monitoring. Added replay/backfill capabilities and clear operational runbooks.

Outcome: Reliable event collection at massive scale with predictable operations and better data quality downstream.