Staff AI Engineer

Drive post-training and API model research for frontier foundation models across reasoning, coding, instruction following, and structured and tool-based generation. Contributed to Structured Outputs, o3-mini training and post-training, and GPT-4.1 research.

Architected evaluation infrastructure processing more than 100K benchmark samples and millions of generated tokens. Built Python tooling for experiment orchestration, benchmark execution, result aggregation, regression analysis, and failure analysis, reducing validation cycles by about 50% and manual evaluation effort by about 40%.

Led technical architecture for high-volume backend services, improving platform scalability by approximately 35% through service decomposition, workload balancing, and performance engineering. Directed architecture reviews and cross-team technical decisions for production services.

Optimized distributed services using Go, Java, Kafka, PostgreSQL, Redis, and Kubernetes. Introduced monitoring, automated recovery, and failure-isolation mechanisms that lowered recurring incidents while improving processing capacity and implementation efficiency.

Delivered highly available backend services supporting millions of daily payment transactions with 99.99% availability across critical workloads. Improved service throughput through concurrency optimization, service decomposition, workload balancing, and backend performance tuning.

Refined database schemas, indexing, query execution, and caching strategies to reduce API latency. Expanded observability across metrics, logging, and distributed tracing, while strengthening automated testing, CI/CD, deployment validation, capacity planning, and reliability practices.

Implemented backend services and APIs for payment workflows and internal platforms, increasing transaction-processing throughput. Created reusable service libraries and platform components that reduced implementation effort across recurring backend capabilities.

Tuned SQL queries, indexes, database access, and caching paths for high-traffic services. Introduced automated testing and continuous deployment practices that reduced production incidents and improved release consistency.

Developed machine learning solutions for real-time dispatch and ETA prediction systems, improving prediction quality and operational efficiency. Applied statistical modeling and optimization to routing, resource allocation, and fleet-utilization problems.

Established end-to-end ML workflows covering data preparation, feature engineering, training, validation, and production integration. Analyzed prediction errors and operational outcomes and partnered with engineering and operations teams to integrate predictive models into real-time workflows.