Experience
Member of Technical Staff — API Capabilities
Dec 2025 – Present
- Core member of the API team shipping frontier model capabilities to enterprise customers via Responses API, powering integrations with industry-leading partners (Cursor, Decagon, Perplexity).
- Designed and delivered new API primitives for tool use, structured outputs, and multi-turn agentic workflows adopted by thousands of developers.
Software Engineer — Applied AI
Apr 2025 – Dec 2025
- Shipped Plaid's first MCP (Model Context Protocol) server in partnership with Anthropic and OpenAI, enabling seamless LLM integration with Plaid's dashboard APIs.
- Designed and productionized a multi-agent framework (LangGraph) powering Plaid's customer support bot; improved ticket deflection by 35%, saving an estimated $2M+ annually.
Software Engineer — Consent Platform (Tech Lead)
Apr 2023 – Apr 2025
- Led a team of 8 engineers owning Plaid's entire authorization surface—APIs, web apps, and SDKs—serving 100M+ end users across 12,000+ financial institutions.
- Scaled core authorization microservice from 3K to 10K+ QPS with 99.95% uptime and sub-50ms p99 latency, processing 2B+ events annually with zero data loss.
- Designed a unified consent management framework adopted cross-team, cutting integration time from 3 weeks to 3 days while ensuring SOC 2 and GDPR compliance.
Software Engineer — Financial Institution Partnerships
May 2021 – Apr 2023
- Built bank-facing API products strengthening data-sharing partnerships with top-10 U.S. banks (Wells Fargo, Citi, US Bank, BofA), expanding institutional coverage by 5%.
- Redesigned end-user authentication flow, improving conversion by ~8% (millions of additional successful connections annually).
Founding Engineer
Dec 2018 – May 2021
- Designed the Contract API for Structured Products and Annuities adopted by 25+ investment banks; cut client onboarding from 2 weeks to 3 days.
- Architected analytics services (back-testing, efficient frontier) handling 800+ QPS at sub-100ms p99, serving $50B+ AUM.
- Built event-driven framework on AWS (Lambda, SQS, EventBridge) reducing computation latency from 24h to 30min (48×).
Quantitative Analyst — Interest Rate Derivatives
Feb 2016 – Dec 2018
- Redesigned volatility surface calibration models (SABR, local vol), improving pricing accuracy by 15% and reducing hedging P&L slippage by 20%.
Skills
AI / ML
LLM APIs, multi-agent systems, LangGraph, MCP, prompt engineering, RAG, embeddings
Backend
Python, Go, Java, TypeScript, gRPC, REST, GraphQL
Infra
AWS (Lambda, SQS, EventBridge, ECS), Kubernetes, Terraform, Datadog
Data
PostgreSQL, DynamoDB, Redis, Kafka, Spark
Education
Carnegie Mellon University
M.S. in Computational Finance
GPA 3.8/4.3 · Dec 2015
Central University of Finance and Economics
B.S. in Mathematical Economics & Finance
GPA 3.82/4.0 · Jul 2014