Rafael Fischer (PhD)

Experience

Generative AI Software Engineer - fischerafael (Worldwide, remote), Jul/2024 - present

Fischerafael is my own freelance venture specializing in AI‑native, LLM‑powered product development for US and European clients, automating workflows and delivering intuitive UX/UI.

LLM‑Driven Applications: Architected and built Next.js and Python apps integrating agentic functionalities, AI workflows with n8n, and seamless cross‑system integrations to solve complex back‑office challenges.
Secure AI‑Driven Development: Leveraged AI tools (Cursor, Claude, Lovable) responsibly—applying clean‑architecture principles, comprehensive test suites (Vitest, Jest, Cypress), and CI/CD pipelines to ensure reliability and compliance.
Remote Collaboration: Partnered asynchronously with startups and SMBs across the US and Europe, consistently delivering user‑centric solutions that work out of the box, driving adoption and measurable ROI.
Continuous Learning & Application: Stay at the forefront of AI engineering through regular courses and hands‑on experimentation, applying cutting‑edge OpenAI, LangChain, and Pydantic AI features to client projects.
Value‑Driven Mindset: Focus on crafting products that are easy to use, performant, and outcome‑oriented—helping clients automate tedious tasks, enhance user experiences, and unlock new revenue streams.

Tech: Next.js, Vercel AI Toolkit, shadcn/ui, TailwindCSS, React Query, Python, OpenAI, LangChain, n8n, Azure Functions, Pydantic AI, MCPs, Cursor, CI/CD, TDD (Vitest, Jest).

Co-Author - AI Engineering in Practice - Manning Publications, Jun/2025 - present

Co-authoring a practical, developer-friendly guide to AI engineering and prompt engineering, targeted at professionals with no prior AI experience.
Designed an accessible chapter structure blending concise introductions, clear theory, and extensive real-world examples of code and prompts.
Developed hands-on exercises (5–7 per chapter) to help readers immediately apply concepts, reinforce learning, and build practical AI skills.
Covered foundational topics including foundation models, prompt design, prompt security, retrieval-augmented generation, prompt evaluation, and optimization.
Balanced depth and clarity to demystify complex AI concepts while keeping the focus on applied engineering practices.
Collaborated with an international co-author and editorial team to ensure consistent voice, technical accuracy, and engaging learning flow.

Tech: RAG, AI Agents, AI Workflows, MCP, Evals