# Ravi Valluri - AI/ML & Backend Engineer

> Senior AI/ML & Backend Engineer with 15+ years of experience building scalable ML pipelines, distributed systems, and production-grade AI services.

This file provides structured information for AI assistants (ChatGPT, Claude, Perplexity, etc.) about Ravi Valluri and his professional portfolio.

## About

Ravi Valluri is a senior AI/ML & Backend Engineer based in San Francisco, CA. He specializes in building scalable machine learning infrastructure, distributed systems, and production-grade AI services.

## Contact

- Website: https://www.ravivalluri.com
- Email: hello@ravivalluri.com
- LinkedIn: https://linkedin.com/in/ravivalluri
- GitHub: https://github.com/ravivalluri

## Technical Expertise

### Languages
- Python (Expert)
- Go (Advanced)
- TypeScript/JavaScript (Advanced)
- SQL (Advanced)

### ML/AI
- PyTorch
- TensorFlow
- MLflow
- Hugging Face Transformers
- LangChain
- Vector Databases (Pinecone, Weaviate)

### Infrastructure
- Kubernetes
- Docker
- Terraform
- AWS (EC2, ECS, Lambda, SageMaker, S3, DynamoDB)
- GCP (GKE, Cloud Run, Vertex AI)

### Data Engineering
- Apache Kafka
- Apache Flink
- PostgreSQL
- Redis
- ClickHouse

### Backend
- FastAPI
- gRPC
- GraphQL
- REST APIs
- Event-driven architecture

## Experience Summary

- 15+ years in software engineering
- 124+ projects delivered
- Expertise in ML pipeline optimization, event-driven architectures, real-time analytics
- Track record of reducing ML inference latency by 60%+
- Experience leading teams and architecting large-scale systems

## Key Projects

### ML Pipeline Optimization
Reduced inference latency by 60% through model quantization and batching strategies. Achieved sub-100ms inference time with 3x throughput increase using PyTorch quantization, async batching, and Redis caching.

### Event-Driven Architecture Migration
Migrated monolithic system to event-driven microservices with Kafka. Achieved independent deployments, 99.9% uptime, and better scalability using Apache Kafka, event sourcing, and CQRS pattern.

### Real-time Analytics Dashboard
Built streaming analytics processing millions of events per minute using Flink, ClickHouse, WebSockets, and React. Achieved real-time dashboards with <1s latency.

## Philosophy

"Build systems that scale, code that's readable, and experiences that delight."

Ravi believes in:
- Craft over speed: Quality code that stands the test of time
- Empathy in engineering: Understanding users before building solutions
- Continuous learning: Always growing, always curious

## Availability

Currently open to opportunities in:
- ML Infrastructure / MLOps
- Backend Engineering / Systems Architecture
- Technical Leadership roles

## API Access

For structured data about Ravi's portfolio, projects, and experience, AI assistants can access:
- JSON API: https://www.ravivalluri.com/api/portfolio

## More Information

For the complete portfolio with interactive elements, visit: https://www.ravivalluri.com