Backend Developer | DevOps Engineer | AI Engineer

I'm a software engineer passionate about building production-grade infrastructure and backend systems that solve real problems in distributed systems, observability, and runtime safety. I specialize in creating robust, scalable solutions designed for engineers who demand reliability and efficiency.

As a Backend Developer, DevOps Engineer, and AI Engineer, I combine systems knowledge with infrastructure expertise and machine learning to build intelligent, self-optimizing services and platforms.

• 🔬 Deep technical expertise in backend systems, containerization, and infrastructure automation
• 🏗️ Production-first mindset — every service I build is battle-tested for real-world use
• 📊 Visible reasoning — systems should explain their decisions, not be black boxes
• ⚡ Performance obsessed — zero unnecessary overhead, minimal dependencies
• 🤖 AI-driven solutions — leveraging ML and causal inference for intelligent automation
• 📖 Clear communication — comprehensive documentation and clear code

Currently: Building intelligent infrastructure automation systems that predict failures before they happen and scale capacity automatically.

I focus on backend services, infrastructure automation, distributed systems, observability, and AI-powered infrastructure. My projects are designed for production use with clean APIs, minimal dependencies, and zero-config deployment.

Predictive auto-scaling for Docker & Kubernetes

An autonomous control system that watches your services, predicts failures before they happen, and scales capacity automatically using control theory (MPC + RL).

• 🔍 No thresholds — uses machine learning to predict problems 60 seconds ahead
• 📊 Live dashboard with failure risk scoring and reasoning feed
• 🏗️ Zero-config: just add one label to your Docker services
• 💾 Works in-memory or with PostgreSQL for persistent history
• 🐳 Kubernetes-ready manifests included

Repository: loadequilibrium
Tech Stack: Go, React, Prometheus, Docker, Kubernetes
Key Feature: Can run in single container or on K8s with one replica

Root cause analysis from system metrics

Identifies true root causes from distributed system metrics with safety-aware decision-making. Uses causal inference to distinguish correlation from causation.

• 🎯 Finds actual root causes, not just correlated symptoms
• ⚡ Real-time inference with low latency
• 🛡️ Safety-aware recommendations that don't break things
• 📈 Works with any Prometheus-compatible metrics source

Repository: Real-time-causal-inference-engine
Tech Stack: Go, Python, Prometheus
Use Case: When you need to know why something failed, not just that it failed

Runtime danger detection in terminal logs

A terminal-native system that preserves your original terminal behavior while detecting and highlighting dangerous runtime signals (crashes, exceptions, timeouts, memory exhaustion) with near-zero latency.

• 👻 Invisible when safe — only highlights actual problems
• ⚡ Near-zero latency — <1ms overhead even under load
• 🎨 Context-aware highlighting based on log severity
• 🔧 Works with any application without code changes

Repository: terminal-log-highlighter
Tech Stack: Rust, Shell
Perfect For: Development, testing, and production log streams

Real-time runtime visualizer for live terminal logs

Converts live terminal logs into structured runtime events and traffic simulation. Bridges the gap between raw logs and actionable insights.

• 📺 PTY-based interactive shell with proper terminal handling
• 🔗 Multiline stack trace aggregation
• 💨 Zero-allocation byte-level classification
• 🎛️ Ring-buffer with async pipeline for backpressure

Repository: logdrive
Tech Stack: Go, TypeScript
Status: Production-grade CLI ingestion foundation

All these projects share a core philosophy:

✅ Zero-config when possible — sensible defaults, minimal setup
✅ Production-grade — built to run in real systems, not just demos
✅ Visible reasoning — systems should explain their decisions
✅ Efficient — designed for production at scale (low overhead, minimal dependencies)
✅ Well-documented — clear READMEs and code comments
✅ Battle-tested — proven in production environments

• Backend Development — scalable services, APIs, microservices, event-driven architecture
• DevOps & Infrastructure — Kubernetes, Docker, containerization, infrastructure automation, monitoring
• AI Engineering — causal inference, anomaly detection, predictive modeling
• Distributed Systems — reliability, fault tolerance, high availability
• Observability — metrics, logging, tracing, alerting systems
• Performance — low-latency systems, memory efficiency, optimization

Each project has comprehensive documentation in its README. Pick one and dive in:

• Want auto-scaling? → Start with LoadEquilibrium
• Need root cause analysis? → Check Causal Inference Engine
• Observability focus? → Try Terminal Log Highlighter or LogDrive

I'm always interested in:

• 💬 Collaboration on backend systems, DevOps challenges, and infrastructure projects
• 🔍 Technical discussions about distributed systems and performance optimization
• 🚀 Production use cases — if you're using these tools in production, I'd love to hear about it
• 🤖 AI initiatives — building intelligent, predictive infrastructure
• 🤔 Feedback to make these tools better

Reach out via GitHub Issues or discussions on any of my repositories.

• Building tools used in production environments solving real infrastructure challenges
• Creating robust, scalable backend services and DevOps solutions
• Making observability more intuitive and actionable for engineering teams
• Demonstrating that production tools can be elegant and user-friendly
• Combining backend expertise with AI to create intelligent, self-optimizing systems

Last updated: 2026-06-04
Built for engineers who need infrastructure that actually works.