Performance-oriented software engineering student with hands-on experience building low-latency data pipelines, microservices, and real-time computation systems in C, C++, Java, and Python. Proven ability to design and o…
Performance-oriented software engineering student with hands-on experience building low-latency data pipelines, microservices, and real-time computation systems in C, C++, Java, and Python. Proven ability to design and optimize computationally intensive systems under strict latency and throughput constraints. Strong background in time-series analysis, systems instrumentation, and rigorous experimental methodology. Eager to apply these skills to trading system development at the intersection of financial markets and high-performance engineering.
Built time-series forecasting and early-risk-detection models on enterprise-scale ERP datasets with non-stationary, noisy, and incomplete signals — analogous to live trading data environments.
Engineered lag-based, rolling-window, and seasonality-decomposed feature sets to capture temporal dynamics; reduced false-positive delay alerts by ∼18% via structured feature ablation.
Implemented reproducible experimentation pipelines with versioned datasets, fixed train–validation splits, and calibration-aware evaluation (ROC-AUC, precision–recall, Brier score).
Designed structured failure-mode analyses to identify uncertainty amplification sources, directly informing downstream system design decisions.
Built time-series forecasting and early-risk-detection models on enterprise-scale ERP datasets with non-stationary, noisy, and incomplete signals — analogous to live trading data environments., Engineered lag-based, rolling-window, and seasonality-decomposed feature sets to capture temporal dynamics; reduced false-positive delay alerts by ∼18% via structured feature ablation., Implemented reproducible experimentation pipelines with versioned datasets, fixed train–validation splits, and calibration-aware evaluation (ROC-AUC, precision–recall, Brier score)., Designed structured failure-mode analyses to identify uncertainty amplification sources, directly informing downstream system design decisions.
Selected from 23 candidates nationwide for a highly competitive internship within the Loyalty Platform team, supporting large-scale subscription and membership systems serving millions of users.
Designed and implemented low-latency backend microservices in Java and Spring Boot, deployed on Kubernetes, with careful attention to throughput, fault tolerance, and response latency under production load.
Instrumented system components with structured telemetry to enable downstream behavioral analysis, reducing mean time-to-diagnosis by ∼35% for latency anomalies in subscription workflows.
Participated in performance-focused code reviews and architectural design discussions with senior engineers, gaining exposure to production-scale system constraints and rollout procedures.
Optimized Cosmos DB query patterns and caching strategies, cutting p99 API latency by ∼20% across critical service endpoints.
Selected from 23 candidates nationwide for a highly competitive internship within the Loyalty Platform team, supporting large-scale subscription and membership systems serving millions of users., Designed and implemented low-latency backend microservices in Java and Spring Boot, deployed on Kubernetes, with careful attention to throughput, fault tolerance, and response latency under production load., Instrumented system components with structured telemetry to enable downstream behavioral analysis, reducing mean time-to-diagnosis by ∼35% for latency anomalies in subscription workflows., Participated in performance-focused code reviews and architectural design discussions with senior engineers, gaining exposure to production-scale system constraints and rollout procedures., Optimized Cosmos DB query patterns and caching strategies, cutting p99 API latency by ∼20% across critical service endpoints.
Benchmarked and optimized ML inference pipelines through systematic preprocessing and architecture experiments, improving runtime efficiency by ∼30% without degrading model accuracy.
Profiled and optimized LLM inference pipelines in a browser-based environment, reducing end-to-end latency by ∼40% through batching and caching strategies.
Designed evaluation protocols measuring latency–quality tradeoffs, establishing quantitative benchmarks across realistic query workloads — transferable directly to trading signal evaluation.
Benchmarked and optimized ML inference pipelines through systematic preprocessing and architecture experiments, improving runtime efficiency by ∼30% without degrading model accuracy.
Profiled and optimized LLM inference pipelines in a browser-based environment, reducing end-to-end latency by ∼40% through batching and caching strategies., Designed evaluation protocols measuring latency–quality tradeoffs, establishing quantitative benchmarks across realistic query workloads — transferable directly to trading signal evaluation.
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