James Chen

With nearly 10 years of experience building large-scale distributed systems using Java, Python, and Go, focused on delivering reliable, scalable platforms for AI-driven products and digital healthcare services. At Google and K Health, I supported systems serving 1M+ daily requests and 100K+ users, improving responsiveness of user-facing applications by up to 25% and enabling real-time access to critical healthcare insights, while maintaining high reliability in regulated environments.

Senior Software Engineer at Google (2022-04 – Present)

Vertex AI - Platform for building, deploying, and running ML models at scale, handling training, inference, and production AI workloads.

• Led Vertex AI model serving infrastructure using Python, Go, gRPC, and Kubernetes (GKE), powering 1M+ daily AI-driven user requests and 100K+ active users, improving end-user experience through faster and more reliable AI product interactions at scale.
• Integrated AI inference capabilities into backend systems using Java, Python, REST/gRPC APIs, and Pub/Sub on GCP, enabling real-time AI-driven features across large-scale services and improving responsiveness of user-facing applications by 25%.
• Built Vertex AI monitoring and observability dashboards using TypeScript, React, and GCP services, improving system visibility across millions of AI interactions and reducing time to detect production issues by 35%, improving service reliability for end users.
• Improved CI/CD and DevOps workflows for AI services using Cloud Build, GitHub Actions, Docker, GKE, and Terraform, increasing deployment reliability to 99%+ and reducing production disruptions by 40%.
• Partnered with ML, infrastructure, and product teams to streamline delivery of AI-powered features, reducing cross-team integration friction and accelerating release cycles for customer-facing capabilities.
• Mentored 6 engineers through code reviews, system design discussions, and debugging support (Java, Python, GCP), helping improve code quality and overall team productivity.

Software Engineer at Google (2019-04 – 2022-06)

Cloud Healthcare API - Service for storing and exchanging healthcare data securely using standards, enabling interoperability between medical systems.

• Built backend services on Google Cloud Healthcare API using Java, Python, and REST APIs, enabling standardized healthcare data exchange across fragmented enterprise systems using FHIR, HL7, and DICOM, improving interoperability across clinical workflows and accelerating cross-system data usability.
• Designed clinical data ingestion pipelines on GCP using Pub/Sub, BigQuery, and Cloud Storage with event-driven microservices architecture, doubling healthcare data processing throughput for enterprise-scale workloads and improving data availability for downstream clinical applications.
• Optimized distributed backend services on GCP using Java microservices and REST APIs, reducing API latency by 25% under high-volume healthcare traffic and improving responsiveness of clinical applications at scale.
• Improved healthcare data processing workflows on Cloud Healthcare API using Pub/Sub-based event-driven systems, increasing system reliability and operational stability under high-throughput, regulated healthcare workloads.
• Implemented secure data access controls on Cloud Healthcare API using GCP IAM, encryption, and compliance-driven access policies, ensuring secure and regulated handling of sensitive clinical data across enterprise systems.

Software Engineer at K Health (2016-12 – 2019-04)

K Health Platform - Digital health application that provides symptom checking and connects patients with doctors through scalable backend systems.

• Built backend services for HIPAA-regulated patient intake and symptom triage systems using Java (Spring Boot), REST APIs, and PostgreSQL, supporting 50K+ patient onboarding sessions per month and improving reliability of clinical data flows by 30% across patient journeys.
• Developed symptom checker and clinical triage backend APIs using Java, Spring Boot microservices, and REST architecture, enabling structured symptom processing and decision routing logic for 100K+ users, improving workflow consistency and scalability of patient-facing healthcare applications.
• Implemented clinical data processing services for patient records and health profiles (PHI systems) using Java-based microservices architecture, handling millions of clinical data records and reducing data retrieval latency by 25% across core healthcare systems.
• Collaborated with product managers, clinical stakeholders, and frontend engineers through API contracts and system design alignment, accelerating delivery of patient journey features and reducing cross-team integration friction for healthcare applications.

Bachelor of Science (B.S.) in Computer Science – University of Illinois Urbana - Champaign (2012 – 2016)