I build production-grade AI systems — from intelligent agents and RAG platforms to autonomous / Physical AI infrastructure.

AI proposes. Deterministic software validates and controls execution.

• 🤖 Agentic AI — agents, orchestration, tools, MCP, memory, evaluation
• 🧠 AI Platforms — RAG, model routing, AI gateways, inference & data pipelines
• 🏗️ Distributed Systems — backend services, event-driven systems, APIs, scalable architectures
• 🛡️ AI Security & Governance — guardrails, policy enforcement, runtime validation, auditability
• 🚗 Physical AI — robotics, autonomous systems, self-driving & safety-oriented software

Orchestra AI — infrastructure between an AI agent's reasoning and real-world actions.

Validate → Execute → Audit

Exploring reliable architectures for Agentic AI, Decision Intelligence, and Autonomous Systems.

Python · C++ · Java · Go · Rust · FastAPI · Spring Boot LangGraph · RAG · MCP · LLMs · Vector Search · AI Evaluation Kafka · Spark · PostgreSQL · Redis · MongoDB AWS · Azure · GCP · Docker · Kubernetes · Terraform · GitHub Actions

AI Systems Architecture · Agent Runtime · Decision Intelligence · AI Security · MLOps · Distributed Systems · Robotics Software · Autonomous Systems

• Orchestra AI — AI action validation, policy, audit & runtime control
• Agentic AI Security — prompt injection, tool abuse, privilege boundaries & red teaming
• Manufacturing AI — industrial intelligence and workflow automation
• AgentOS Patterns — reusable patterns for production agent systems
• MCP Workflows — AI-to-tool and AI-to-business-process architectures

AI / Agent
    ↓
Plan → Route → Execute
    ↓
Validate → Policy → Observe
    ↓
Action → Audit

I use AI for ambiguity and deterministic software for guarantees.

Open to AI engineering, architecture, technical advisory, startup and founding-engineer opportunities.

GitHub · LinkedIn

Build intelligent systems. Make them trustworthy. Make them real.