I build backends that stay up under load.

Scalable Python microservices on FastAPI, AsyncIO and gRPC inside an event-driven architecture — sustaining high-volume traffic with reliable uptime while cutting response latency.

• Integrated ML and generative-AI endpoints for content recommendation, moderation and metadata summarization into backend services over REST, streamlining inference handling across creator-facing apps.
• Built caching and persistence layers with Redis, PostgreSQL and Cloud Storage for high-volume retrieval APIs — less database load and lower average query latency.
• Automated cloud-native delivery on GCP with Docker, Kubernetes and GitHub Actions, shortening release cycles and hardening rollbacks.
• Rolled out distributed tracing with OpenTelemetry and Grafana Tempo, reducing time to diagnosis during peak traffic.
• Hardened REST and LLM inference endpoints through PyTest suites, profiling and code review, improving code quality.

Modernized enterprise Java and Spring Boot services out of a legacy monolith into distributed, event-driven components — secure REST APIs processing high-volume daily transactions with faster response times.

• Wired scikit-learn fraud-detection models into Kafka-driven Python workflows, using feature engineering to reduce false positives.
• Tuned Redis caching alongside Oracle Database indexing and query optimization for high-traffic transaction endpoints, lowering latency and raising peak throughput.
• Automated OKE environments with Docker, Terraform and Jenkins pipelines, improving infrastructure utilization and release preparation.
• Centralized security logging and audit trails in ELK and Splunk, flagging anomalous access and shortening investigations.
• Raised API security standards with Spring Security and OAuth while practicing TDD with JUnit and Maven, improving productivity.