Software Engineer | Backend & Distributed Systems | AI/ML Enthusiast
I am a results-driven Software Engineer with a proven track record of building scalable distributed systems and AI-powered solutions. My experience spans from refactoring monoliths into high-performance microservices at Vision Verse to developing GenAI-driven incident response systems at Amazon.
- 🔭 Current Focus: Scaling distributed architectures and implementing LLM-based automations.
- 🚀 Past Impact: Reduced data latency by 92% and automated 40% of L2 support tickets at scale.
- 🎓 Education: Master of Information Technology.
- ✍️ Research: Published author in IEEE International Conference.
- Languages: Ruby, C#, Java, Python, TypeScript, C++
- Frameworks: Ruby on Rails, Node.js, Spring Boot, Sidekiq, RabbitMQ
- Performance: Refactored monoliths to SOA, reducing latency from 2s to 150ms.
- GenAI: Building automated incident response systems using LLMs and CloudWatch logs.
- Deep Learning: Developed Fraud Detection engines with 94% precision using TensorFlow/PyTorch.
- Platforms: AWS (Lambda, EC2, S3, DynamoDB, CloudWatch), Azure, GCP
- Practices: Serverless Architecture, CI/CD (Jenkins), Infrastructure Cost Optimization.
- Frontend: Next.js, React, Vue.js, GraphQL
- Databases: PostgreSQL, MongoDB, Elastic Search, DynamoDB, Redis
- At Amazon: Prevented $2.1M/quarter in fraud through Deep Learning models.
- At Vision Verse: Optimized architectures to support 100k+ Daily Active Users (DAU).
- Cost Efficiency: Reduced AWS infrastructure costs by 20% through serverless optimization.
"Building the future, one microservice at a time."