I design and build production-oriented software systems where backend engineering, intelligent automation and AI work together to solve operational problems.

My public work demonstrates backend services, workflow orchestration, LLM-powered incident diagnosis, computer vision pipelines and machine learning systems designed with reliability, explainability and deterministic control as first-class engineering requirements.

I engineer operational software systems that combine backend engineering, intelligent automation and applied AI to make reliable decisions in real-world environments. Across these systems, AI is integrated as a decision-support capability within architectures governed by determined workflows, confidence decision logic and human oversight where reliability matters.

My engineering work includes::

• Backend platforms that orchestrate APIs, business workflows, operational processes and distributed services.
• Intelligent decision systems that combine software engineering, machine learning and deterministic business logic.
• LLM-powered applications using Retrieval-Augmented Generation (RAG), vector search, confidence scoring, approval workflows and audit trails.
• Computer vision systems for automated inspection, operational validation and quality assurance.
• Workflow orchestration and rule-based automation engines for real-world operational environments.
• Machine learning systems designed with explainable predictions, deterministic safety constraints and measurable evaluation.
• Modular software architectures that separate business logic, AI inference and decision enforcement for scalability and maintainability.