- π― Building scalable backend systems for fintech & fraud detection
- π§ Strong focus on Graph ML, real-time pipelines, and explainable AI
- βοΈ Love designing end-to-end architectures (API β DB β ML β UI)
- π India
struct Abhishek {
string focus = "Backend + AI Systems";
vector<string> domains = {
"Fraud Detection",
"Fintech Infrastructure",
"Graph Neural Networks",
"Distributed Systems"
};
string current_mission = "Building production-grade AI systems";
};Python, Neo4j, PyTorch Geometric, n8n
- π Unified 500+ accounts & 2,500+ transactions into entity graph
- π€ Hybrid GraphSAGE + GAT model for mule detection
- β‘ Sub-second fraud alerts using automated pipelines
- π Explainability via SHAP + gradient XAI
- π Privacy layer using SHA-256 anonymization
Node.js, PostgreSQL, Redis, Python
- πΈ Simulated UPI, card, wallet flows
- π§ Built ghost transaction detector
- π AI-powered ledger reconciliation system
Next.js, Node.js, TypeScript, MySQL, Prisma, Docker
- π³ Fully containerized with Docker Compose
- π JWT auth + role-based system
- π€ Integrated AI recommendations + chatbot
Next.js, React, TypeScript, Tailwind CSS
- β‘ Build portfolios in under 2 minutes
- π GitHub auto-sync (90% manual effort reduced)
- π§ Dynamic dashboard + customization engine
π B.Tech Computer Science β GGSIPU π CGPA: 8.04/10

