Available for new opportunities

Shireen Meher
Chirravuri

Backend engineer with 4+ years building high-scale systems in fintech and search. Currently exploring reliability and factuality in LLMs as an AI Research Intern at Meta, and pursuing my MS in Computer Science at UMass Amherst. I care about how systems behave in production — especially when they don’t haha :)

View My Work Get In Touch ↓ Resume

What I Work With

Languages
Python Java Golang JavaScript TypeScript C PHP
Infrastructure & Cloud
AWS Docker Kubernetes CI/CD Lambda SQS SNS
Databases
Neo4j PostgreSQL MongoDB MySQL
Search & Backend
Elasticsearch OpenSearch Flask REST APIs Laravel Retool
AI & ML
LLMs OpenAI API Semantic Search Reinforcement Learning kNN
Concepts
Distributed Systems System Design DBMS OOP DSA Microservices

My Obsessions

🌐
Distributed Systems
I enjoy building systems where correctness depends on coordination across machines. I’ve worked on leader-based architectures, replication flows, and async pipelines using queues and events. What interests me most is how systems behave under failure - retries, partial outages, and consistency trade-offs, and designing them to remain reliable at scale.
🗄️
Databases & Storage
I’m drawn to the internals of databases — how indexing, query planning, and storage design impact performance at scale. I’ve worked with Neo4j and ElasticSearch, experimenting with graph traversals, full-text search, and relevance tuning. I like getting into the details of why queries are slow, how indexes change behavior, and how real systems balance performance with consistency.
⚙️
Performance & Architecture
I’m interested in how systems behave under load — where latency builds up, how contention shows up, and what actually moves the needle. At Razorpay, I worked on optimizing concurrency in a high-throughput service, reducing P99 latency in the critical path. I like reasoning about trade-offs in caching, parallelism, and async processing based on real system behavior, not just theory.

Work Experience

AI Research Intern
Meta
Jan 2026 – Present
FactCurriculum: Adaptive Verifiable Factual Training to Reduce Hallucinations in LLMs
  • Designing FactCurriculum, an adaptive auto-verifiable factual training framework for LLMs using KB-backed QA tasks and Reinforcement Learning to study hallucination reduction and confidence calibration.
  • Building an evaluation pipeline to measure factual accuracy, hallucination rate, and Expected Calibration Error (ECE) against SFT baselines on 1B-scale models.
Software Engineer II
Venwiz
Mar 2023 – Jan 2025
Category Connect & Search Relevance Platform
  • Designed an event-driven ingestion pipeline (Lambda + SQS + SNS) to process 0.7–1M vendor records, choosing async fan-out to handle bursty workloads and avoid backpressure on downstream search indexing.
  • Built a hybrid search system in Elasticsearch combining OpenAI embedding-based semantic search with Lucene kNN and keyword relevance scoring, improving coverage and relevance for category-based vendor discovery.
  • Solely owned the Neo4j graph database with complex APOC-based traversals, efficient indexing, and schema customization for industrial procurement search.
  • Designed relevance signals and ranking strategies achieving 70% relevance across production queries; built a self-serve graph and tag management system using Python Flask + Retool.
  • Integrated GPT-based APIs to auto-generate and enrich vendor metadata, strengthening discovery and downstream search relevance.
Software Engineer
Razorpay
Jul 2021 – Feb 2023
Payment Systems & Platform Modernization
  • Improved high-throughput payment and merchant activation flows via goroutine-based concurrency and replica database configurations, reducing P99 latency by 45% and support tickets by 35%.
  • Designed and productionized Rule-Based Method Enablement, a real-time event-driven system that automatically configures merchant payment methods based on dynamic business parameters using Laravel events.
  • Led monolith-to-microservices API decomposition, resolving data mismatches and fixing 341 failing unit tests to stabilize releases and improve service isolation.

Featured Projects

Research
🧠
FactCurriculum
Adaptive, auto-verifiable factual training framework for LLMs using KB-backed QA tasks and Reinforcement Learning to study hallucination reduction and confidence calibration on 1B-scale models.
📈
Distributed Stock Trading Platform
Fault-tolerant trading system with replicated order service using leader-based routing, re-election, and log replay for crash recovery under concurrent writes. LRU caching with server-driven invalidation deployed on AWS.
💸
BudgetBuddy
Full-stack AI-powered budgeting app with React Native frontend, Flask backend, and Firebase. Features expense tracking, smart categorization, analytics, and budget management with Strategy pattern for extensible filtering.
🌐
Grounded Entity Search
Converts topic queries into structured entity tables using web search and LLM-based extraction with source verification — grounding AI outputs in real-time web evidence.
📚
CF AI Study Planner
Edge-native AI application using Cloudflare Agents for personalized study planning with persistent memory and durable workflows — built at the edge for low-latency planning experiences.
Personal Portfolio Website
Interactive portfolio showcasing my work in backend systems, distributed architecture, and search platforms. Designed with custom animations, dynamic UI elements, and a focus on clean, intuitive presentation.

Education

Master of Science
Computer Science
University of Massachusetts Amherst
Jan 2025 – Dec 2026 (Expected)
GPA: 4.0 / 4.0
Bachelor of Technology
Indian Institute of Technology Bhubaneswar
2017 – 2021

Say Hello

I'm open to backend, distributed systems, or AI engineering roles. Whether you have an opportunity, a question, or just want to chat tech — my inbox is always open.