Software Engineer with hands-on experience building scalable backend services and distributed systems. Strong in Java, SQL, and Linux, with solid CS fundamentals and a passion for writing high-quality code that scales.

• 🎓 Computer Science @ California State University, Long Beach (Dec 2025)
• 🔭 Currently working on full-stack applications and AI-powered solutions
• 💡 Passionate about distributed systems, microservices, and DevSecOps
• 🌱 Always learning and exploring new technologies

1. Problem-First Thinking

• Start by deeply understanding the problem and user needs
• Define clear success metrics and technical requirements
• Research existing solutions and identify gaps

2. Architecture & Design

• Design scalable, maintainable architecture from the ground up
• Choose the right tech stack based on project requirements, not trends
• Plan for failure: implement error handling, logging, and monitoring early

3. Iterative Development

• Build MVPs to validate ideas quickly
• Write clean, documented code with meaningful comments
• Implement CI/CD pipelines from day one for rapid iteration

4. Security & Performance

• Security scanning integrated into every build (SonarQube, Trivy)
• Performance monitoring and optimization as core features
• Regular code reviews and quality gates

5. Continuous Learning

• Document learnings and challenges faced
• Refactor and improve based on real-world usage
• Share knowledge through clear README files and documentation

Tech Stack: Next.js 14, TypeScript, React, Tailwind, PostgreSQL, Plaid, Dwolla

Key Learnings:

• Financial API Integration: Mastered Plaid API for multi-bank connectivity and Dwolla for ACH transfers
• Security Best Practices: Implemented JWT authentication, secure token handling, and PII data protection
• Database Optimization: Wrote complex SQL queries for transaction history, learned PostgreSQL indexing strategies
• State Management: Handled complex real-time state updates for account balances and transactions
• CI/CD at Scale: Set up GitHub Actions → Netlify pipeline with automated testing and deployment

Tech Stack: AWS EC2, Kubernetes, Jenkins, Docker, SonarQube, Trivy, ArgoCD, Prometheus, Grafana

Key Learnings:

• DevSecOps Pipeline: Built complete CI/CD with security gates, blocking 15+ vulnerabilities before deployment
• Container Orchestration: Deep dive into Kubernetes deployments, services, ingress, and Helm charts
• Infrastructure as Code: Automated AWS infrastructure setup and configuration
• Monitoring & Observability: Set up Prometheus metrics collection and Grafana dashboards for system health
• GitOps Workflow: Implemented ArgoCD for declarative, version-controlled deployments
• Security Scanning: Integrated Trivy for container vulnerability scanning and SonarQube for code quality

Tech Stack: Next.js, TypeScript, OpenAI API, Pinecone, LangChain

Key Learnings:

• Vector Databases: Implemented semantic search using Pinecone for contextual professor matching
• AI Integration: Learned prompt engineering and fine-tuning for consistent, accurate responses
• RAG Architecture: Built Retrieval-Augmented Generation system with LangChain for grounded AI responses
• Sentiment Analysis: Developed NLP pipeline for analyzing review sentiment and generating ratings
• API Rate Limiting: Managed OpenAI API costs and implemented caching strategies

Tech Stack: Next.js, Firebase, Material-UI, OpenAI API, GCP Vertex AI

Key Learnings:

• Real-time Database: Mastered Firebase Firestore for live inventory updates and subscriptions
• AI-Powered Features: Integrated multiple AI APIs for recipe generation based on available ingredients
• Image Recognition: Used GPT Vision API for food item identification from photos
• UI/UX Design: Learned Material-UI design patterns and responsive layouts
• Search Optimization: Implemented advanced filtering, sorting, and search algorithms

• Advanced Distributed Systems Architecture: Exploring patterns like event sourcing, CQRS, and saga patterns
• Cloud-Native Development: Deep diving into AWS services, serverless architectures, and microservices patterns
• AI/ML Integration: Building production-ready AI applications with LangChain, vector databases, and LLM orchestration
• Performance Optimization: Studying advanced caching strategies, load balancing, and database optimization
• System Design: Mastering scalability patterns, CAP theorem, and distributed consensus algorithms

I'm always open to interesting conversations and collaboration opportunities!

• 📧 Email: dhrumitsavaliya26@gmail.com
• 💼 LinkedIn: linkedin.com/in/dhrumit-savaliya
• 📱 Phone: 562-515-5080