🎓 MS in Computer Science @ Arizona State University (2027)
🔧 Interested in distributed systems, ML systems, Full stack and backend engineering
📈 Focused on building reliable, observable, and scalable data platforms
- Designing distributed event-driven systems using Kafka and transactional SQL databases
- Building multimodal RAG pipelines for text and image understanding
- Exploring consistency, sharding, and fault tolerance in production-style systems
A fault-tolerant, Kafka-based streaming platform for ingesting and observing real-time financial market data.
- Tech: Apache Kafka, CockroachDB / PostgreSQL, Python, Docker, Prometheus, Grafana
- Highlights:
- Event-driven ingestion with deterministic ordering per symbol
- Strong consistency using serializable transactions
- Multi-layer sharding (Kafka partitions + distributed SQL ranges)
- Production-style observability for throughput, failures, and system health
A retrieval-augmented generation (RAG) system supporting semantic search across documents and images.
- Tech: FastAPI, Docker, PostgreSQL + pgvector, CLIP embeddings, OCR, Angular & React micro-frontends
- Highlights:
- Cross-modal (text ↔ image) retrieval using CLIP embeddings
- ANN indexing (IVF / HNSW) for low-latency similarity search
- Context-grounded LLM responses with citations
- Modular architecture designed for extensibility
Languages
Python, TypeScript, JavaScript, SQL
Distributed Systems & Data
Apache Kafka, CockroachDB, PostgreSQL, pgvector, Redis
Backend & Infrastructure
FastAPI, Docker, GitHub Actions, Prometheus, Grafana, REST APIs
ML Systems
PyTorch, HuggingFace, CLIP, RAG pipelines, Multimodal LLMs
Frontend
React, Angular, Next.js, Micro-frontends
Cloud
AWS (S3, EC2, RDS, IAM), GCP (Firebase Auth, Firestore, Crashlytics)
- Advanced Kafka stream processing and backpressure handling
- Multimodal embedding and retrieval strategies
- Distributed database design and CAP trade-offs