Backend & DevOps-oriented engineer building scalable, production-grade distributed systems across the stack — from low-level RISC-V architecture to cloud-native infrastructure.
Experienced in designing and deploying systems using Kubernetes, GitOps workflows, ML pipelines, and observability stacks, with a strong focus on performance, reliability, and self-healing systems.
Strategically aligned towards cloud infrastructure, systems engineering, and fintech platforms, with hands-on expertise in building resilient backend architectures and automated infrastructure pipelines.
| Languages | Backend | Frontend |
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| AI / ML | DevOps & Cloud | Observability |
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| Systems / Hardware / Blockchain | ||
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Production-grade Kubernetes GitOps system with automated recovery
Highlights:
- ArgoCD-based continuous deployment pipelines
- Chaos Mesh failure simulation (resilience testing)
- Full observability stack (Prometheus + Grafana)
- Self-healing infra with automated recovery
Automated failure detection and remediation insights
Highlights:
- Detects CrashLoopBackOff and system anomalies
- Automated diagnostics pipeline
- Improves debugging workflows in containerized systems
NLP-based threat detection and classification
Highlights:
- Scalable ML inference pipeline
- Security-focused NLP classification
🔗 https://github.com/ServerCrash358/ThreatFind
6-class transformer-based NLP classifier
Tech: PyTorch • Transformers • NLP
Real-time application with scalable frontend + backend
Tech: React • Firebase • Real-time architecture
- Cloud-native infrastructure & DevOps automation
- Distributed systems & backend scalability
- RISC-V and systems experimentation
- Fintech & blockchain (smart contracts, infra design)