- 🔭 Built 3 production-grade systems serving 1,000+ users
- 🌱 Learning advanced distributed computing with PySpark and cloud-native architectures
- 💼 Former AI/ML Intern at Zeronsec - processed 15GB+ daily cybersecurity data
- 🎯 Interested in MLOps, Big Data, and Scalable System Design
- 🏆 3 Certifications: AWS Cloud Practitioner + 2x Oracle Cloud AI/ML Professional
- 📫 Reach me at: krishnajan2004@gmail.com
| 🚀 Metric | 📊 Achievement |
|---|---|
| Deployment Platform | Zero-downtime builds, 100+ concurrent workflows |
| AI Chatbot | 1,000+ queries handled, 95% satisfaction rate |
| Pipeline Optimization | 60% throughput improvement, 65% faster processing |
| Certifications | AWS + Oracle Cloud AI/ML Professional (2x) |
Microservices architecture handling 100+ concurrent builds with zero-downtime
A full-stack deployment platform with automated build workflows and live domain routing
- Built with Next.js 15 (App Router) and Node.js featuring responsive admin dashboard with TailwindCSS, Shadcn/ui, and Framer Motion
- Designed microservice architecture with Redis queues managing parallel build workflows and persistent deployment history
- Integrated GitHub OAuth, auto-builds with support for 10+ frameworks (React, Vue, Next.js, Angular)
- Achieved cost-free scalability using Cloudflare R2, Upstash Redis, and Render
- Tech: TypeScript, Next.js, Node.js, React, Redis, Cloudflare R2, Docker
Handling 1,000+ queries with 95% user satisfaction and <3s response time
Intelligent AI chatbot for Indian legal assistance with emergency contact integration
- Developed using FastAPI and Gemini AI to explain Indian laws with comprehensive ticket system and admin escalation
- Implemented OTP-based verification and role-based access control to prevent spam
- Built automated weekly data cleanup using APScheduler
- Integrated location-based emergency contact feature for 50+ city zones
- Tech: Python, FastAPI, React.js, MongoDB, Docker, Gemini AI
65% faster processing with advanced ML across 16+ heterogeneous data sources
Distributed log analysis system using advanced ML for anomaly detection
- Designed with PySpark and parallel processing to handle diverse sources (Windows, Linux, Hadoop, HDFS, Spark)
- Implemented and benchmarked 9+ ML models including DANN-BERT, LoRA-BERT, and Hybrid-BERT architectures
- Achieved scalable cross-domain generalization using hybrid embeddings and adversarial domain adaptation
- Optimized pipelines through multi-core parallelism
- Tech: PySpark, Python, TensorFlow, BERT, Scikit-learn, Hadoop, HDFS
- 🤖 Real-time anomaly detection system with sub-second latency
- 📊 MLOps pipeline with automated model versioning and monitoring
- 🌐 Distributed task scheduler using Redis Streams
- ⚡ High-performance data ingestion pipeline for IoT devices
- ☁️ AWS Certified Cloud Practitioner - Amazon Web Services | Verify
- 🤖 Oracle Cloud Infrastructure 2025 Generative AI Professional - Oracle | Verify
- 📊 Oracle Cloud Infrastructure 2025 Certified Data Science Professional - Oracle | Verify
AI/ML Intern @ Zeronsec (May 2024 - July 2024)
- Built data pipeline processing 15GB/day of cybersecurity logs using schema validation and streaming
- Improved throughput by 60% via bottleneck analysis and optimized parallel ETL workflows
- Automated data preprocessing reducing manual effort by 45% and designed scale-up plan for 50+ GB/day
B.Tech in Computer Science Engineering
SRM University AP | Oct 2022 - Aug 2026
🔧 MLOps & ML Infrastructure Projects | 🌐 Large-Scale Distributed Systems
🤖 AI-Powered Developer Tools | 📊 Real-Time Data Processing Pipelines
