Zen, a former GitHub engineer, reviews two software engineers’ resumes aiming to pivot into AI engineering, advising them to create focused narratives centered around relevant AI projects, quantify achievements, and streamline content to fit a single page. He emphasizes showcasing tangible AI skills through standout projects, integrating certifications contextually, avoiding generic statements, and tailoring resumes to clearly demonstrate their AI engineering potential.
In this video, Zen, an ex-offer engineer at GitHub who now helps people land AI engineering roles, reviews two resumes from individuals attempting to pivot their software engineering careers into AI engineering. He emphasizes the importance of tailoring resumes to highlight relevant experience succinctly, ideally fitting everything onto a single page. For the first resume, belonging to a software architect with over ten years of experience, Zen points out that much of the content is irrelevant to AI roles and that the resume is unnecessarily lengthy. He advises focusing on clear ownership of projects, quantifying achievements, and creating a narrative that bridges past fintech work to AI engineering, possibly through independent AI projects that showcase relevant skills.
Zen critiques the first resume’s structure, suggesting reducing bullet points for older roles and consolidating redundant sections like skills and education to save space. He highlights the lack of a clear story leading to AI engineering as a major weakness, recommending the candidate develop a standout AI-related project to serve as a “North Star” on the resume. This project could demonstrate practical AI engineering skills and help connect the candidate’s past experience with their desired future role. Overall, Zen believes that with these adjustments, the candidate can significantly improve their chances of transitioning into AI engineering.
The second resume, from a cloud support engineer with about two years of experience, is praised for including links to LinkedIn, GitHub, and project validations, which add credibility. However, Zen criticizes the professional summary for being vague and filled with generic phrases that don’t add value. He suggests moving the skills section lower on the resume and instead leading with a strong AI-related project that clearly demonstrates relevant capabilities. This approach would make the skills evident through the project description, avoiding redundant or fluffy statements. Zen also notes the resume is concise and well-formatted but needs reframing to align previous cloud and data infrastructure experience with AI engineering ambitions.
Zen further advises integrating certifications into the employment history to save space and provide context, rather than listing them separately. He appreciates the slim education section on this resume and encourages the candidate to leverage their focus on scalable, robust systems by developing AI projects that showcase these strengths. Such projects could highlight the candidate’s ability to build secure, scalable AI data pipelines, making their resume more compelling for AI engineering roles. Zen stresses that having a clear, focused narrative and a keystone AI project is more important than nitpicking individual bullet points.
In conclusion, Zen notes that both candidates have solid technical backgrounds but need to reframe their resumes around a clear AI engineering persona supported by tangible projects and relevant experience. He warns against excessive corporate jargon and encourages embedding hyperlinks to projects and certifications to provide proof of skills. Zen invites viewers to join his AI engineer program for personalized coaching and access to resume writing masterclasses, tools, and strategies to accelerate their transition into AI engineering roles.