25+ years of experience designing mission-critical backends. Operating at the intersection of large-scale systems, cloud platforms, and Agentic AI.
The Architect
With over 25 years of experience in software development and enterprise architecture, I operate at the intersection of large-scale systems, cloud platforms, and Artificial Intelligence. As an Enterprise AI Architect, I help organizations design, govern, and scale AI-enabled solutions that are secure, resilient, and fully integrated into complex enterprise ecosystems.
My background as a Solution Architect spans J2EE, SOA, and modern cloud platforms such as AWS and OpenShift, with a strong foundation in microservices architecture and Domain-Driven Design (DDD). This allows me to architect AI systems that are not only technically robust, but also aligned with business domains, regulatory constraints, and long-term enterprise strategy.
In recent years, I have expanded my focus to Generative AI and agentic system architectures, designing and implementing AI-driven workflows based on autonomous and semi-autonomous agents. I work with LLMs, agent orchestration patterns, and tool-augmented reasoning, integrating GenAI capabilities into existing enterprise platforms to improve decision-making, automation, and developer productivity.
A key area of expertise is prompt engineering at enterprise scale, including prompt design, chaining, optimization, evaluation, and versioning. I apply structured prompting techniques and reusable prompt patterns to ensure reliability, consistency, and explainability of AI outputs in production environments, with a strong focus on risk mitigation and alignment with business objectives.
Core Competencies
Designing secure, governed, and resilient AI systems fully integrated into complex, heavily regulated enterprise ecosystems.
Implementing autonomous and semi-autonomous AI workflows using advanced LLM orchestration and tool-augmented reasoning.
Architecting scalable backends on AWS and OpenShift using Domain-Driven Design and microservices principles.
Structured prompt design, versioning, evaluation, and optimization to ensure reliable and explainable AI outputs.
Focus Areas
Building systems where AI components plan, reason, and act to solve complex enterprise problems.
Enhancing LLM retrieval with knowledge graphs to provide accurate, context-aware answers over private enterprise data.
Establishing frameworks for AI security, observability, cost control, and regulatory compliance.
Let's build scalable, governed, and secure AI systems together. Connect with me to discuss enterprise architecture and Agentic AI.