Shreya C ShreyAC962

whoami

class ShreyaChinthala:
    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"

    def what_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.

Current focus

🔭 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)

Building blocks

Languages & Backend

Distributed Systems & Data

Generative AI

Cloud & DevOps

Observability & Security

Proficiency status bar

Domain	Level	Signal
Java / Spring Boot Microservices	`██████████` Senior	3+ yrs, Microsoft + JPMorgan 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

Featured case studies

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

Beyond the code

📄 distributed-systems papers 🧩 system-design puzzles 🤖 tinkering with AI agents 🛠️ open-source ☕ over good coffee

🤝 Quick connect

_{⭐ Open to backend / distributed-systems / AI-platform roles. If a repo here is useful, a star means a lot.}

Navigation Menu