I'm a systems engineer obsessed with building production-ready, observable AI systems. My focus spans architectural depth, defensive design, and trade-off analysis across latency, cost, and accuracy.

• 🏗️ Systems-First Thinking — Designing resilient, observable AI systems with measurable SLOs
• 🛡️ Defensive Engineering — Building graceful failure recovery and fault tolerance into production systems
• 🔍 Anomaly Detection & Fault Prognosis — Real-time monitoring using symbolic filtering and diagnostic systems
• 📊 MLOps Excellence — Production ML pipelines with observability at core
• 🎯 Trade-off Analysis — Optimizing latency, cost, and model accuracy for real-world constraints
• 🤝 HITL Systems — Human-in-the-loop feedback loops for continuous system improvement
• 📐 Architecture Documentation — Clear system design for complex distributed systems
• 🌍 Contributing to open-source systems engineering and AI infrastructure

Interests: AI Systems Engineering • MLOps • Production ML • Distributed Systems • Fault Detection • Observability • SLO Engineering • Causal Inference • Symbolic Methods

🎯 NeuralBudget	📐 AI Architecture Blueprints
SLO engineering framework for production ML systems spanning traditional software and MLOps. Precision, reliability, and architectural depth.	Systems-first engineering for production-ready agentic AI. Observability, defensive design, and trade-off analysis guides.

🔍 Fault Oracle	🚫 NoTears DAG Learning
Rust-based symbolic dynamic filtering for real-time anomaly detection and fault prognosis in complex systems.	Rust implementation of NO TEARS continuous optimization for causal structure learning in DAGs.

• Design and implement observable, fault-tolerant AI systems at scale
• SLO engineering spanning ML models, inference pipelines, and infrastructure
• Architectural patterns for agentic AI and multi-step reasoning systems

• Real-time anomaly detection using symbolic dynamic filtering
• Fault prognosis and predictive maintenance in complex systems
• Causal reasoning for root cause analysis

• Model deployment pipelines with observability built-in
• Latency-accuracy-cost optimization
• Continuous monitoring and drift detection

• Distributed systems design and architecture
• Defensive programming practices
• Trade-off analysis documentation

Languages

ML & Data

MLOps & Observability

Frameworks & Tools

Symbolic & Causal Methods

"Systems are defined by their constraints, not their capabilities."

I believe in building AI systems with:

• Observability First — If you can't measure it, you can't understand it
• Defensive Design — Plan for failure modes and recover gracefully
• Trade-off Transparency — Make explicit choices between latency, cost, and accuracy
• Human-in-the-Loop — Leverage human judgment where AI has uncertainty
• Causal Reasoning — Go beyond correlation to understand system behavior

• 🔨 Building robust SLO frameworks for production ML systems
• 📊 Implementing real-time anomaly detection for complex distributed systems
• 🎯 Designing agentic AI architectures with observability at core
• 📚 Documenting systems engineering best practices for AI

"Complexity is the enemy of reliability. Simplicity is the path to understanding."