Xiao Yuan Kong

• Worked on the Data Science team.

• Built Smart Answers, an API using Python and FastAPI, that leverages Large Language Models (LLMs) to assist customers in answering questionnaires based on historical data.

• Transitioned a manually-run Jupyter notebook developed by data scientists into an automated, production-ready scheduled job using Apache Airflow, Databricks, Python, PySpark, Pytest, Terraform, and Jenkins.

• Optimized a Spark pipeline processing terabytes of data, reducing costs from over $10k to $50 and increasing speed by over 50x.

• Enhanced data science workflow efficiency and minimized manual intervention by developing and refining CI/CD processes to automate builds and deployments, thereby supporting MLOps initiatives.

• Owned and led the creation, operation, and maintenance of critical infrastructure projects and automation for the data science team.

Technologies: Python (FastAPI, PySpark, Pytest, LangChain, LlamaIndex), AWS (Bedrock, Lambda, ECS, SQS, SageMaker, RDS, Cloudwatch), Databricks, Airflow, Terraform, Kafka, Jenkins, Kubernetes, Datadog.

Worked on Downstream, a data-driven SaaS platform that provides insights, business intelligence, and automation for Amazon and Walmart sellers and vendors.

• Developed, scaled, and maintained a serverless data pipeline that collects and ingests gigabytes of data daily from Amazon and Walmart APIs.

• Enhanced user experience by reducing the P90 response time of REST API endpoints by over 70%.

• Increased system throughput by making architectural improvements leveraging SQS, ECS, and Kinesis.

• Accelerated time-to-market by implementing CI/CD pipelines and Infrastructure as Code (IaC) using AWS CDK and CloudFormation.

• Improved system visibility by creating Cloudwatch and Datadog alarms and dashboards.

• Conducted code reviews and mentored other engineers.

• Supported Customer Success Managers (CSMs) by addressing customer questions and issues.

Technologies: Python (Django, Pandas, Pytest), Node.js, AWS (Lambda, ECS/Fargate, DynamoDB, Redshift, Redis, RDS, Kinesis, SQS, SNS, Step Function, Cloudwatch, S3, CodePipeline, Cloudformation/CDK). Monitoring (Datadog, Sentry).

Worked on the platform team.

• Standardized AWS infrastructure code and improved developer experience by developing an internal IaC library for JavaScript and Python on top of AWS CDK.

• Unblocked developers by assisting with IaC and CI/CD implementation.

• Supported other teams in resolving production issues and identifying systems bottlenecks.

• Defined and shared software engineering best practices across the organization.

Technologies: Javascript/Typescript (Node.js), Python, AWS, CDK/Cloudformation, Docker.