Jason Guo

Senior Backend Engineer at Medely (2023-04 - Present)

Built backend systems for Talent Fusion, a real-time healthcare staffing platform, using Python, Go, Node.js, AWS, Kubernetes, Kafka/SQS, Redis, and PostgreSQL.
Designed and delivered an AI-driven shift-matching engine with LLM-powered feature extraction, improving match accuracy by 35% and reducing time-to-fill for urgent roles.
Developed Go-based event workers using goroutines, channels, worker pools, and context cancellation to process high-volume staffing events reliably.
Built Python ETL and LLM pipelines with schema validation, content hashing, caching, idempotency, and cost controls to improve feature quality and reduce duplicate LLM calls.
Built and maintained Node.js APIs for matching, booking, notifications, and analytics dashboards, supporting real-time staffing workflows.
Improved API performance through Redis caching, rate limiting, database indexing, and query optimization, reducing average response latency by 30%.
Deployed services on AWS Kubernetes infrastructure using Docker, Terraform, and GitHub Actions, reducing release cycles by 50%.
Improved production reliability with Datadog dashboards, distributed tracing, structured logging, and alerting, helping maintain 99.9%+ uptime.

Software Development Engineer III at Amazon (2019-04 - 2023-03)

Built and scaled backend services using Django + DRF, powering real-time IoT telemetry systems.
Engineered a high-throughput ingestion pipeline (Django, Celery, AWS IoT Core, Kinesis, Lambda) processing 50M+ events/day.
Developed real-time dashboards with Django Channels, WebSocket, and React, reducing equipment downtime by 25%.
Architected serverless microservices using API Gateway, DynamoDB, PostgreSQL, Step Functions, cutting infrastructure costs by 40%.
Built data lake pipelines using Python, S3, and Athena, reducing reporting time from hours to minutes.
Applied CQRS and modular Django app design to improve system maintainability and scalability.
Strengthened API and platform security with OAuth, RBAC, IAM boundaries, and encryption, reducing vulnerabilities by 40%.
Enhanced observability via CloudWatch, structured logging, custom middleware, and tracing, improving issue resolution speed by 60%.
Co-led backend architecture reviews to ensure fault tolerance, scalability, and SLA compliance.
Expanded automated testing (pytest, Django test framework) and CI pipelines, achieving 98%+ deployment success.

Software Engineer at Deloitte (2017-01 - 2019-03)

Designed and built distributed microservices in Java with Spring Boot, Kafka, Redis, and Postgres serving millions of daily requests with 99.99% uptime.
Developed Kafka producers/consumers for event-driven workflows processing high-volume streaming data with guaranteed delivery and ordering semantics.
Architected and maintained RESTful APIs supporting real-time operations for customer-facing systems.
Implemented performance optimizations including JVM tuning, thread pool sizing, Redis caching, and query optimization, reducing API latency by 40%.
Deployed and operated cloud-native applications on AWS EKS, managing auto-scaling policies and rolling upgrades with zero downtime.
Built automated testing frameworks using JUnit, TestNG, and K6, achieving 90% coverage for critical services.
Integrated OpenTelemetry and Grafana dashboards to improve observability and reduce MTTR by 50%.
Used AI-assisted coding tools (Claude, Copilot) to accelerate development, test generation, and code refactoring.
Supported production incident response, leveraging Splunk for log analysis, heap dump inspection, and root-cause troubleshooting.
Led architecture reviews and mentored junior engineers on microservices, Kafka, and cloud best practices.

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