classShreyaC:
def__init__(self):
self.role="Software Engineer"self.focus= ["Distributed Systems", "Cloud-Native Backend", "Applied GenAI"]
self.stack= ["Java", "Spring Boot", "Python", "FastAPI", "Kafka"]
self.superpower="wiring RAG + MCP into real distributed systems"self.education="MS CS, Santa Clara - AI & Scalable Distributed Systems"defwhat_i_do(self):
return"design event-driven microservices that stay correct under load, " \
"then teach them to reason with AI - without breaking governance."I build the unglamorous half of software that has to be right: exactly-once payment ledgers, idempotent APIs, zero-downtime key rotation. Lately I bring the same rigor to AI on the backend - governed RAG pipelines, Model Context Protocol tooling, and semantic caching that cuts model cost.
- 🔭 Building secure AI features with Azure OpenAI · LangChain · RAG, connected to enterprise workflows over the Model Context Protocol (MCP)
- ⚡ Scaling event-driven services on Apache Kafka with an eye on p99 latency and throughput
- 🔐 Hardening APIs with Entra ID · OAuth 2.0 · JWT · Key Vault
- 📈 Watching everything through OpenTelemetry · Prometheus · Grafana
- 🌱 Learning deeper vector search (pgvector) and agentic patterns (LangGraph)
Languages & Backend
Distributed Systems & Data
Generative AI
Cloud & DevOps
Observability & Security
| Domain | Level | Signal |
|---|---|---|
| Java / Spring Boot Microservices | ██████████ Senior | 3+ yrs, production systems |
| Distributed Systems & Kafka | █████████░ Advanced | Event-driven, exactly-once, chaos-tested |
| Cloud-Native (Docker / K8s / CI-CD) | █████████░ Advanced | AKS/EKS, GitHub Actions, Jenkins |
| Applied GenAI (RAG / MCP / pgvector) | ████████░░ Strong | Shipping AI features at Microsoft |
| Python / FastAPI | ████████░░ Strong | AI service layer, tooling |
| Observability (OTel / Prom / Grafana) | ████████░░ Strong | Production monitoring & incident response |
Recruiter-grade, benchmarked, and honestly documented - each solves one expensive problem.
| Project | What it proves | Stack |
|---|---|---|
| llm-guard-gateway | Prompt-injection firewall + semantic cache: p50 44 ms → 4.6 ms | FastAPI · pgvector · MCP |
| txn-exactly-once-ledger | 46 process kills, 0 lost, 0 double-applied transfers | Postgres · Kafka · outbox |
| cdc-read-model-projector | Byte-identical replay; fixes the out-of-order-commit CDC bug | Postgres · CQRS · CDC |
| saga-chaos-lab | 7 crash points, 7 consistent recoveries | Saga · chaos engineering |
📄 distributed-systems papers🧩 system-design puzzles🤖 tinkering with AI agents🛠️ open-source☕ over good coffee
