
AI Native Engineer Product Engineering, Implementation & Quality Assurance Coder & Technical PM Every developer is a technical product manager, with AI handling the heavy lifting of implementation, the focus has shifted heavily towards defining the “what” and “why” with extreme precision. The craft of software development is evolving – from typing code to articulating intent. When your source of truth is what you want built, not just how it’s written, AI becomes an amplifier. Developers evolve into orchestrators: designing specs, guiding agents, and validating outcomes. It’s not less creative – it’s more creative. I work with AI continuously using the Research, Planning, Implementation (RPI) flow, focussing on designing effective context engineering strategies for both full projects and specific tasks. Through clear and precise guidance AI produces efficient, readable, and maintainable solutions without unnecessary complexity. Quality is controlled through a combination of proper architecture, context, research, planning, and scope specification, combined with automated checkpoints, manual reviews, and testing processes. Coding with AI is significantly faster, and quality is maintained through these structured processes. My number one priority is providing proper specifications and context, enabling AI to deliver production-worthy code. RPI Flow: Continuous integration of Research, Planning, and Implementation in every task. IDE & CLI: VSCode, AntiGravity, and ClaudeCode are used extensively. Custom Setup: Custom agents, model selection, instructions, and skills setup (Manual and Cloud). Agentic development: Combining multiple AI agents for task-specific workflows. MCP Servers: Context and agentic workflows. AI Assistants: Copilot as a base, with experience in Cursor, Windsurf, Cline, and Roo Code. Context Engineering: Smarter AI through precise specifications and context storage. AI Dev Implementation Experience Full Stack RAG: Implemented a custom RAG AI Chat in this portfolio website. (Tool use ongoing) Rapid Prototyping: Fast-paced development and iteration with AI. Agentic Systems: Built and deployed agentic development systems. Context-Rich Environments: Selective and context-aware work environments for smarter AI. Adaptability: Early adopter, first AI development in 2022, remaining up-to-date in a fast-moving space. Contact Delft, The Netherlands linkedin/robertjansen hire@robertjansen.dev Style Preferences Theme Palette Appearance Text Smoothing Corner Smoothing

AI Native Engineer

Product Engineering, Implementation & Quality Assurance

Coder & Technical PM

Every developer is a technical product manager, with AI handling the heavy lifting of implementation, the focus has shifted heavily towards defining the “what” and “why” with extreme precision.

The craft of software development is evolving – from typing code to articulating intent. When your source of truth is what you want built, not just how it’s written, AI becomes an amplifier. Developers evolve into orchestrators: designing specs, guiding agents, and validating outcomes. It’s not less creative – it’s more creative.

I work with AI continuously using the Research, Planning, Implementation (RPI) flow, focussing on designing effective context engineering strategies for both full projects and specific tasks. Through clear and precise guidance AI produces efficient, readable, and maintainable solutions without unnecessary complexity.

Quality is controlled through a combination of proper architecture, context, research, planning, and scope specification, combined with automated checkpoints, manual reviews, and testing processes.

Coding with AI is significantly faster, and quality is maintained through these structured processes. My number one priority is providing proper specifications and context, enabling AI to deliver production-worthy code.

RPI Flow: Continuous integration of Research, Planning, and Implementation in every task.
IDE & CLI: VSCode, AntiGravity, and ClaudeCode are used extensively.
Custom Setup: Custom agents, model selection, instructions, and skills setup (Manual and Cloud).
Agentic development: Combining multiple AI agents for task-specific workflows.
MCP Servers: Context and agentic workflows.
AI Assistants: Copilot as a base, with experience in Cursor, Windsurf, Cline, and Roo Code.
Context Engineering: Smarter AI through precise specifications and context storage.

AI Dev Implementation Experience

Full Stack RAG: Implemented a custom RAG AI Chat in this portfolio website. (Tool use ongoing)
Rapid Prototyping: Fast-paced development and iteration with AI.
Agentic Systems: Built and deployed agentic development systems.
Context-Rich Environments: Selective and context-aware work environments for smarter AI.
Adaptability: Early adopter, first AI development in 2022, remaining up-to-date in a fast-moving space.