Open to opportunities

Zhang Xiao

@zhangxiao

Message

Senior backend engineer building cloud-native microservices and production AI systems.

United States

Message

What I'm looking for

I’m looking for a role where I can own backend architecture for high-throughput, event-driven systems and integrate production-ready ML/NLP features on AWS—shipping reliable, low-latency services with strong engineering leadership.

I’m a Senior Backend & Full-Stack Engineer with 7+ years building scalable distributed systems and high-performance, cloud-native backend infrastructure using Python and Java. I focus on REST and GraphQL API design, event-driven architecture, and AWS deployments across high-throughput production environments.

I integrate ML-powered capabilities into real systems—building NLP pipelines and vector search features that reliably support high-volume workloads. In my most recent role, I owned production backend microservices and delivered sub-150ms p95 latency with 99.9%+ uptime while driving measurable performance and efficiency improvements across caching, databases, CI/CD, and observability.

I also lead AI systems work end-to-end as a Founder & Lead Engineer, architecting institutional-grade quantitative trading infrastructure. I’ve built low-latency processing with Vert.x, accelerated backtesting in C++ (20x speedup), and implemented Kafka/RabbitMQ-based pipelines and orchestration—plus REST/GraphQL strategy management with ML inference behind the API surface.

My background includes a PhD in Computer Science focused on machine learning and natural language processing, with experience spanning applied research and internship work in data science, computer vision, and large-scale search relevance. I bring a technical leadership mindset—mentoring engineers, raising architectural quality early, and shipping systems that stay reliable under real load.

Experience

Work history, roles, and key accomplishments

Current

Founder & Lead Engineer

Current

Independent Venture (Confidential)

Jan 2024 - Present (2 years 5 months)

Architected institutional-grade quantitative trading infrastructure, splitting responsibilities between Python for ML/strategy logic and Java for execution services and APIs. Achieved 20x faster backtesting (6 hours to 18 minutes) and built sub-100ms event-driven market data processing with 99.9%+ availability.

Python Java C++Vert.X Kafka RabbitMQ GraphQL WebSockets

Current

Backend & AI Engineer

Current

Amazon

Jun 2020 - Present (6 years)

Owned production Python (FastAPI) and Java (Spring Boot) microservices, delivering REST APIs for 10+ internal teams with sub-150ms p95 latency and 99.9%+ uptime. Reduced API response overhead by 35% with Redis/PostgreSQL/MongoDB optimizations and cut data retrieval latency by 45% by delivering an internal GraphQL data access layer.

Python Java fastAPI Spring Boot REST APIs GraphQL AWS Kafka PostgreSQL Redis

Board Advisor (ML & Backend)

Stealth Startup

May 2024 - Apr 2025 (11 months)

Provided senior technical guidance to shape ML infrastructure and backend architecture decisions for an early-stage AI product. Developed the foundational blueprint for scalable data pipelines, model deployment, and inference workflows while guiding event-driven vs API-driven architecture choices in Python and Java.

System Design Distributed Systems Event Driven Architecture API Architecture Python Java ML Infrastructure AI Model Deployment Roadmapping

Applied Scientist Intern

A9.com

May 2019 - Aug 2019 (3 months)

Developed machine learning models in Python and Java to improve search ranking and retrieval relevance for large-scale advertising/search systems. Built click-through rate feature engineering pipelines and supported A/B testing workflows to inform ranking algorithm updates.

Python Java Machine Learning Search And Ranking Search Relevance Feature Engineering A B Testing Data Pipelines

Research Associate Intern

HP Labs

May 2017 - Aug 2017 (3 months)

Conducted applied research on computer vision and deep learning for industrial inspection, training and evaluating image classification/detection models to improve quality inspection accuracy. Built C++/MFC desktop tooling to visualize and iterate on model outputs with reduced manual inspection overhead.

Computer Vision Deep Learning TensorFlow Caffe Object Detection C++MFC Model Evaluation

Data Scientist Intern

Midcontinent Independent System Operator (MISO)

Jun 2014 - Aug 2014 (2 months)

Built Python time-series forecasting models to support real-time electricity dispatch decisions across a multi-state grid. Applied machine learning to large-scale grid datasets and streamlined recurring analysis/reporting workflows to improve turnaround time for operations analysts.

Python Time Series Analysis Machine Learning Data Analysis Data Pipelines Model Evaluation ETL ELT

Education

Degrees, certifications, and relevant coursework

Purdue University

PhD, Computer Science (Machine Learning & NLP)

2010 - 2014

PhD in Computer Science focused on Machine Learning and Natural Language Processing. Dissertation topic: Flexible Structured Prediction in NLP with Partially Annotated Corpora.