The Implementation Skills You Need To Become An AI Developer


Zen van Riel - Senior AI Engineer

Zen van Riel - Senior AI Engineer

Senior AI Engineer & Teacher

As an expert in Artificial Intelligence, specializing in LLMs, I love to teach others AI engineering best practices. With real experience in the field working at big tech, I aim to teach you how to be successful with AI from concept to production. My blog posts are generated from my own video content which is referenced at the end of the post.

Becoming an AI developer doesn’t require years of theoretical study. The fastest path focuses on implementation skills - building complete, working systems that solve real problems. This practical approach creates developers who can deliver immediate value in the workplace.

Beyond Theory-Heavy Pathways

Traditional paths to becoming an AI developer often fail because they:

  • Overemphasize mathematical foundations before practical application
  • Focus on understanding models rather than building systems
  • Delay implementation until “fundamentals” are mastered
  • Neglect production considerations and deployment skills

These approaches create knowledge without practical capability.

Implementation-First Career Path

A more effective approach to becoming an AI developer:

  • Start by building complete working systems, even simple ones
  • Learn concepts as they become relevant to implementation
  • Focus on production considerations from the beginning
  • Develop system design skills alongside model understanding

This approach builds practical capabilities immediately applicable to job requirements.

Essential Implementation Skills

Focus your learning on capabilities employers actually need:

  • System design that integrates AI components effectively
  • Data processing and vector storage implementation
  • Deployment infrastructure and monitoring
  • Performance optimization in resource-constrained environments

These skills address the challenges companies face when deploying AI solutions.

Structured Learning With Community

The fastest path combines implementation focus with community:

  • Following structured learning pathways designed by practitioners
  • Receiving feedback on implementation approaches
  • Learning from others solving similar challenges
  • Building a portfolio of completed implementation projects

This approach simultaneously develops skills and evidence of those capabilities.

Ready to become an AI developer through an implementation-focused approach? Join the AI Engineering community for structured learning pathways designed by practitioners who’ve successfully built careers in AI development, with clear guidance on developing in-demand implementation skills.