Senior Software Engineer at NVIDIA

• Designed and shipped a React/Next.js dashboard for market charts and watchlists that streams tick data over WebSocket and updates 50+ chart components per second with virtualized rendering. • Built Node.js microservices to ingest and normalize multiple exchange feeds (REST + WebSocket), implemented deduplication and out-of-order handling using Redis streams and reduced data gaps from 120s to under 2s. • Integrated an ML inference pipeline (Python, PyTorch) for short-term signal scoring; served predictions via a FastAPI endpoint and added request caching in Redis to keep median response under 120ms. • Implemented GraphQL and REST APIs for user portfolios and historical-series queries; added pagination and TimescaleDB hypertables for efficient time-range scans up to 3 years of tick data. • Containerized services with Docker, defined Kubernetes manifests and Helm charts, and ran load-tested deployments on AWS EKS to validate sustained connections for 2k concurrent chart clients. • Led the realtime-charting performance effort: replaced SVG rendering with canvas-backed D3 layers and cut frame drops under heavy load from 18% to below 2% on target browsers. • Authored CI/CD pipelines (GitHub Actions) for build, test, and deploy; added contract tests for exchange adapters and automated schema checks for incoming market payloads. • Participated in on-call rotations and incident postmortems; reduced mean time to detect for data-feed outages by adding Prometheus metrics and CloudWatch alarms tied to feed latency thresholds. • Mentored two mid-level engineers on frontend architecture, code review, and TypeScript typings for complex financial types; established a shared types package consumed by frontend and backend. • Implemented wallet-connect UI flows and integrated Ethers.js for optional on-chain trade logging and user wallet linking for Web3 features. Technologies: React, Next.js, TypeScript, Node.js, FastAPI, Redis, PostgreSQL, TimescaleDB, Docker, Kubernetes, AWS, PyTorch

Senior Software Engineer at Google

• Led a cross-team effort at Google to build a responsive analytics UI (React, TypeScript) for visualizing large-timeseries datasets and added virtualized tables to handle 100k+ row result sets. • Developed backend services in Go and Node.js to aggregate streaming telemetry into PostgreSQL and BigQuery; designed schemas and ETL jobs that cut query cost by 30% for common analytics queries. • Built a realtime pub/sub layer using gRPC and WebSocket gateways to push alerts and chart updates to clients with end-to-end latency targets under 200ms for priority events. • Integrated ML-derived features produced by an internal PyTorch pipeline; exposed model outputs through a versioned REST API and added A/B routing to compare models in production. • Owner for service reliability for a set of backend APIs: set SLOs, added synthetic tests, and created automated failover for regional database replicas using read-replicas and failover scripts. • Designed and executed a migration from monolithic deploys to containerized microservices (Docker, Kubernetes), establishing deployment patterns and runbooks for rollout and rollback. • Wrote unit and integration tests (Jest, pytest), increased front-end test coverage to 68% for critical components, and introduced contract tests for API consumers. • Collaborated with security teams to add OAuth2 flows and hardened API rate limits and request validation to prevent malformed market data from propagating. • Contributed to internal developer tools including CLI helpers for data replay and local mocking of exchange feeds to speed feature development and debugging. • Reviewed and merged high-impact PRs, coached engineers on performance profiling in Chrome and flamegraph analysis for Node.js services. Technologies: React, TypeScript, Go, Node.js, PostgreSQL, BigQuery, gRPC, Docker, Kubernetes, Jest, pytest, Prometheus

Software Engineer at Bloomberg

• Implemented REST APIs in Python (Flask) and Node.js to serve market snapshots and historical bars to trading clients, supporting 100+ requests/sec during peak periods. • Designed schema for PostgreSQL time-series data and prototyped a rollup process to aggregate tick data into minute/hour bars stored as Parquet on S3 for long-term analytics. • Built a WebSocket gateway to stream live price updates to web clients and a lightweight JavaScript charting layer; reduced client bandwidth by batching updates and delta-encoding messages. • Worked on latency-sensitive pieces of the data pipeline: optimized PostgreSQL queries, added indexes and partitioning, and reduced median query time for 1m-bar lookups from 450ms to 80ms. • Automated deployments with Docker and Jenkins, maintained CI jobs and release notes, and handled production incidents as part of an on-call rotation. • Collaborated with product and quant teams to translate trading requirements into API contracts and implemented server-side validation to catch bad data before ingestion. • Added monitoring (Grafana, Prometheus) for pipeline throughput and set alerts for queue growth and lag, enabling a 40% reduction in missed data windows. • Wrote client SDK examples for the public API in JavaScript and Python to accelerate partner integrations and developer onboarding. • Conducted code reviews and mentored junior engineers on backend best practices and SQL tuning. • Led a refactor to isolate exchange adapters behind a plugin interface, making it possible to add new exchange integrations without downtime. Technologies: Python, Flask, Node.js, PostgreSQL, S3, Parquet, WebSocket, Docker, Jenkins, Grafana