About

Overview

I help engineering teams and organizations evolve towards AI-driven software engineering, improving how complex software systems are designed, built and delivered at scale.

My career has evolved through multiple stages: from frontend engineering and UX systems, to enterprise software architecture, and more recently into AI-driven software engineering and intelligent development systems.

Spec-Driven Development

I apply Spec-Driven Development as a systematic approach to software delivery. Instead of starting from implementation and deriving specifications, the process is inverted: specifications are defined first and drive implementation, testing, and validation.

This approach delivers measurable benefits:

• Reduced ambiguity: Clear specifications eliminate interpretation gaps between business, architecture, and engineering teams
• Accelerated onboarding: New team members can understand system behavior from specs without reading implementation code
• Automated validation: Specs become executable contracts that validate implementation automatically
• AI-ready workflows: Well-structured specifications enable LLMs to generate accurate, context-aware implementation code

Combined with AI tools, Spec-Driven Development creates a powerful feedback loop: specifications inform AI generation, AI output is validated against specs, and validation results refine both the implementation and the specifications themselves.

AI Integration in Daily Workflow

Rather than treating AI as a standalone tool, I embed it into every stage of the engineering lifecycle:

• Architecture & Design: LLMs assist in exploring trade-offs, generating ADRs, and validating architecture decisions against established patterns
• Code Generation: AI generates implementation code from specs, with human review focused on architectural correctness and business logic
• Code Review: AI agents perform first-pass review for style, anti-patterns, and security — human reviewers focus on semantics and design
• Testing: Automated test generation from production code and API contracts, with AI-suggested edge cases
• Documentation: AI-generated documentation from code and specs, kept in sync with implementation
• Quality Gates: AI-powered regression analysis and semantic quality evaluation in CI/CD pipelines

Enterprise Architecture & SAP CX (Spartacus)

Currently, I work at Accenture , where I design and lead large-scale enterprise commerce platforms based on SAP CX Commerce Cloud Composable Storefront (Spartacus) and Angular ecosystems.

I operate across multiple engineering teams and enterprise delivery environments, transforming traditional software delivery models into AI-driven engineering systems focused on scalability, performance and maintainability.

Engineering Philosophy

I apply a systems-thinking approach, connecting architectural decisions with scalability, performance, developer productivity, and long-term system sustainability.

My leadership style is people-first and business-oriented. Technical excellence must be aligned with business outcomes to create lasting value. I believe in building engineering systems where quality is not enforced but enabled — through clear specifications, automated guardrails, and continuous improvement.

Current Focus

• AI-driven software engineering and automation systems
• Agent-based architectures and autonomous workflows
• Developer productivity and AI-augmented engineering environments
• Scalable enterprise architectures enhanced with AI capabilities

Mission

I collaborate with engineering teams, mentor developers and contribute to building high-performance engineering systems focused on scalability, quality and continuous improvement.

My goal is to help organizations evolve into AI-driven engineering systems that enable faster delivery, higher quality and sustainable software architectures.