Applying AI to modern engineering, with measurable results.
Hands-on experience applying AI-assisted engineering, intelligent automation, and developer productivity platforms to help organizations move faster and operate smarter.
Hands-on experience includes evaluating and implementing AI-assisted workflows for architecture planning, code generation, documentation, SQL optimization, debugging, API development, and technical decision support.
Focus is placed on practical AI adoption: improving developer speed and quality while maintaining human oversight, security awareness, governance, and measurable business value.
Where AI Changes the Work
AI-Assisted Engineering
Architecture planning, code generation, debugging, and API design accelerated by coding assistants and LLM workflows.
Coding Assistant Evaluation
Hands-on evaluation of Claude Code, GitHub Copilot, Cursor, and Gemini for real engineering workloads, with working knowledge of Amazon Q Developer.
Developer Productivity Strategy
Practical adoption plans that measure delivery improvement rather than tooling for its own sake.
Enterprise AI Workflow Design
Designing AI-enabled workflows that integrate with existing business processes, data, and systems.
Governance & Human Oversight
AI governance awareness and responsible adoption practices that keep humans in the review loop.
AI Tools Ecosystem
Working fluency across the current generation of AI development and automation tooling.
AI Tools Ecosystem
Platforms evaluated through hands-on engineering work:
Working knowledge / market familiarity:
Bring AI-Assisted Engineering to Your Team
Open to conversations about developer productivity strategy and enterprise AI workflow design.