AI-assisted development, GenAI engineering, Architecture-as-Code, and Azure cost optimization for enterprise teams
AI-assisted development (also called GenAI engineering in the enterprise) means integrating coding agents and tools—Cursor, Copilot, Claude Code, MCP—into your SDLC with architecture, specs, and tests as guardrails. I help teams get velocity without shipping expensive chaos.
Architecture-as-Code means you capture architecture as machine-readable, version-controlled artifacts—C4 models, ADRs, domain specs, non-functional requirements, and fitness functions—instead of slide decks that drift. Those artifacts become the source of truth for humans and AI agents: rules, specs, and CI/CD checks keep generated code inside approved boundaries so what you design stays aligned with what actually runs.
Azure cost optimization finds waste in compute, databases, and Kubernetes, then rightsizes and schedules resources so spend matches real usage. In prior banking roles I delivered roughly $1M/year Azure savings and identified $3M more via a recommendation engine—your results depend on the estate.
Build robust, scalable, and future-proof systems by incorporating non-functional requirements from the start. I help you design systems that are not only functional but also performant, secure, and scalable.
With the power of AI-assisted development, I deliver complete full stack applications in a fraction of the time traditional developers require. Leveraging the latest AI tools and my broad technical expertise, I help clients accelerate digital innovation without sacrificing quality or maintainability.
With AI, I enable organizations to move at start-up speed—secure, reliable, and with all the essential engineering disciplines built in from day one.
Direct answers on AI-assisted development, GenAI engineering, Architecture-as-Code, and Azure cost optimization
AI-assisted development is the practice of using generative AI tools—coding agents, IDEs like Cursor, Copilot, and MCP—to accelerate software delivery while keeping architecture, tests, and quality gates in control. Done well, it amplifies experienced engineers; done poorly, it creates fast technical debt.
GenAI engineering applies software engineering discipline to generative AI in the SDLC: context engineering, agent rules, evaluation, security, and CI/CD integration. It is the enterprise layer above ad-hoc prompting—how teams ship reliable software with GenAI, not demos.
Architecture-as-Code means you capture architecture as machine-readable, version-controlled artifacts—C4 models, ADRs, domain specs, NFRs, and fitness functions—instead of slide decks that drift. Those artifacts become the source of truth for humans and AI agents: rules, specs, and CI/CD checks keep generated code inside approved boundaries so design stays aligned with what actually runs. See the Equal Experts write-up and presentation.
Azure cost optimization starts with inventory and usage analysis, then rightsizing, schedule automation, and transparent savings reporting. At prior banking employers, I delivered roughly $1M/year Azure savings via underutilized resources and identified $3M in further potential through a recommendation engine—results vary by environment.