AI Engineer.

Describe prompt engineering as a systematic discipline, not casual usage. Mention evaluation frameworks you built, A/B tests on prompt variants, and measurable quality improvements. Include specific techniques like chain of thought, few shot learning, or structured outputs that you applied in production systems.

Traditional ML experience is valuable but not always required. AI engineering increasingly focuses on integrating and orchestrating foundation models rather than training from scratch. Highlight ML fundamentals if you have them, but prioritize production integration, evaluation, and deployment experience for most AI engineer roles.

Use professional, readable fonts like Calibri, Arial, or Garamond at 10 to 12 point size for a AI Engineer resume. Stick to black text, clear section headers, and generous white space. Avoid decorative fonts, bright colors, and complex layouts that can cause ATS parsing errors.

A strong AI Engineer resume should include a clear professional summary, relevant work experience with quantified achievements, a skills section tailored to the job description, and education or certifications. Focus on outcomes and impact rather than listing responsibilities.

Portfolios are highly valued for AI Engineer roles in creative, design, or technical fields. If your work is visual or project based, include a link to your portfolio in the resume header. Even if not required, a portfolio demonstrates initiative and gives hiring managers a deeper look at your capabilities.

A reverse chronological format works best for most AI Engineer resumes because it highlights your career progression. Use clean, consistent formatting with clear section headers, bullet points for achievements, and standard fonts. Avoid graphics, tables, and columns that can confuse ATS systems.

Quantify your achievements with specific numbers, percentages, or dollar amounts. Tailor your resume to each job description, use action verbs, and ensure clean formatting. According to Orbyt's resume analysis, resumes with quantified results get 40% more callbacks.