Ruby M — Software Engineer
I build distributed systems that handle real load and I optimize them At PayPal I worked on tokenization and card issuance flows sitting in the critical path of transactions, services processing 3–5K TPS at peak with tens of millions of card lifecycle events moving through Kafka pipelines daily. I brought p95 latency down from ~200ms to ~120ms by introducing Redis caching, fixing connection pooling, and eliminating GC pressure from unnecessary object creation. On the Kafka side, I tuned batch consumption, parallelized consumers, and tightened retry/backoff logic, cutting consumer lag by 60% under peak load. Reliability held at 99.95%+ uptime through idempotency keys and Resilience4j circuit breakers. At American Express I worked on card servicing APIs running at 1–2K RPS, supporting millions of cardholders across transaction lookups and account management flows. I identified a bottleneck in DB call patterns and serialization overhead, added caching for reference data, optimized query execution with proper indexing, and reduced payload size, bringing p95 from ~280ms down to ~160ms, a 40% improvement. On the batch side I built validation and pre-check layers that reduced batch failure rates by 30–40%. On infrastructure I’ve cut CI/CD pipeline times from 18–20 minutes to 9–11 minutes by parallelizing test stages and introducing caching in GitHub Actions. Right-sized Kubernetes pods across services to reduce over-provisioning by 15–20%. Added staged rollouts with Helm and improved health check configurations, reducing failed deployments by ~30%. Set up Prometheus and Grafana alerting that gave teams earlier visibility into error spikes and latency degradation, reducing critical incidents by 25–30% over time. My stack spans Java, Kotlin, Spring Boot, Vert.x, Kafka, gRPC, Flink, React, Angular, NgRx, Terraform, Kubernetes, GCP, AWS, BigQuery, Snowflake, Couchbase, and more recently LangChain and RAG pipelines for AI-integrated backends. Outside of work I’m the sole engineer behind Errnd, a hyperlocal P2P marketplace built on React Native, Firebase, and Stripe Connect. I optimized the RAG layer to cut response latency from 3–4 seconds to under 2, and reduced redundant embedding computation by 60–70% through smart caching. Every architectural decision is mine. That kind of ownership changes how you think about building software. Let’s connect if you want to chat tech, solve problems, or share memes, I’m always up for a good conversation about making things better!
Stackforce AI infers this person is a Fintech expert with strong capabilities in distributed systems and cloud-native architectures.
Location: Phoenix, Arizona, United States
Experience: 5 yrs 1 mo
Skills
- Java Development
- Microservices
- Tokenization
- Java Full Stack Development
- Cloud Computing
- Ci/cd
Career Highlights
- Reduced p95 latency from 280ms to 160ms at American Express.
- Cut CI/CD pipeline times from 20 minutes to 11 minutes at PayPal.
- Sole engineer behind Errnd, optimizing response latency significantly.
Work Experience
PayPal
Software Engineer 2 (1 yr 3 mos)
Software Engineer 2 (5 mos)
BCG X (Boston Consulting Group – Digital)
Software Engineer (8 mos)
American Express
Software Engineer 2 (1 yr 4 mos)
Infosys google
Java full stack developer (1 yr 5 mos)
Infosys
Intern - Full stack developer UI/UX specialist (5 mos)
Vignan's Foundation for Science, Technology & Research
Full Stack Developer (1 yr 10 mos)
Deputy Secretary General (10 mos)
Education
Master's degree at University of Memphis
Bachelor of Technology - BTech at Vignan's Foundation for Science, Technology & Research
