Software Engineer II | 3+ Years of Experience | Distributed Systems Enthusiast
I build highly scalable, distributed systems that process millions of events daily. Currently engineering supply chain visibility solutions, working across microservices, cloud migrations, and ML-powered platforms.
📍 Based in Chandigarh, India
- 🔭 Building ML-powered ETA prediction systems processing 50M+ daily events
- ☁️ Led cloud migration of 20+ microservices from AWS to Azure
- ⚡ Designing APIs handling 10,000+ requests/min with sub-500ms p99 latency
- 🚀 Migrated services from Elastic Beanstalk to Kubernetes (EKS)
- 📊 Optimized Kafka pipelines saving $100K+ annually
- 🤖 Built Ocean-Nexus, an AI diagnostic tool reducing debugging time by 75%
- 🔍 Implemented observability across 12 microservices, reducing MTTR from 2hrs to 30min
Languages:
Backend & Frameworks:
Cloud & DevOps:
Databases:
Messaging & Streaming:
Monitoring & Observability:
Testing & Tools:
| Repository | Description |
|---|---|
| design-patterns-java | 🎯 Comprehensive collection of 20+ Gang of Four design patterns implemented in Java with practical examples |
Engineered end-to-end ML-based ETA/ETD prediction system processing 50M+ daily shipment events. Built APIs handling 10K+ req/min with p99 < 500ms. Reduced prediction variance by 40%, improving on-time delivery by 90%.
Tech: Java, Python, Kafka, Spring Boot, ML Models
Migrated 20+ microservices from AWS to Azure. Implemented blue-green deployments with ArgoCD CI/CD pipelines. Achieved 99.9% uptime during transition.
Tech: Kubernetes, Docker, ArgoCD, Azure AKS, AWS EKS
Upgraded 11 critical services to Spring Boot 3.3/Java 21 and Java 25. Reduced memory by 50%, CPU by 85%. Eliminated 80% of security vulnerabilities.
Tech: Java 21, Java 25, Spring Boot 3.3, JVM Tuning
Migrated Cloudera to Confluent Kafka. Redesigned data pipeline architecture handling 100M+ daily events with zero downtime. Saved $100K+ annually in infrastructure costs.
Tech: Confluent Kafka, Event-Driven Architecture
Developed observability platform using Prometheus, Grafana, and custom Go services for predictive system monitoring. Implemented ML-based anomaly detection reducing false positive alerts by 80%. Integrated Slack and PagerDuty APIs for automated incident routing.
Tech: Go, Prometheus, Grafana, PagerDuty, Slack APIs
Built an AI-powered diagnostic tool leveraging historical incident data for root cause analysis. Reduced average debugging time by 75% through machine learning algorithms.
Tech: Python, ML Algorithms, Historical Data Analysis
- Distributed Systems Architecture - Building scalable, fault-tolerant systems
- Microservices Design - Service decomposition, API design, event-driven patterns
- Cloud Migrations - AWS to Azure, monolith to microservices
- Platform Modernization - Java/Spring Boot upgrades, containerization, Kubernetes adoption
- Streaming Platforms - Kafka pipelines processing 100M+ events/day
- Observability - Distributed tracing, metrics, alerting
- System Design - High-level & low-level design, scalability patterns
Bachelor of Engineering in Computer Science
Chandigarh University, Punjab (2019-2023)
Relevant Coursework: Data Structures, Algorithms, DBMS, Software Engineering, Operating Systems, OOP
- 🏆 CodeChef 4-Star (Rating: 1850)
- 🥇 Rank 555/10,000+ in Inter-NIT Coding Competition
- 📜 Python Data Structures - Coursera
- 📜 HackerRank Problem Solving Certified
- 📜 HackerRank Python Certified
- 📧 praveen230102@gmail.com
- 💬 Ask me about: Distributed Systems, Microservices, Cloud Migrations, Kafka, Kubernetes, System Design
⚡ Fun Fact: From processing 50M+ daily events to debugging with AI—I turn complex distributed problems into elegant solutions, one microservice at a time.
"Just a developer shipping systems that scale" 🚀