Resume and Portfolio Prep Guide for Tech Roles

Resumes and portfolios are the typical first signal employers see. Strong resumes get past initial screening; portfolios provide evidence of work for roles where it matters (engineering, design, AI/ML, technical writing). This guide covers resume-and-portfolio preparation for tech roles, grounded in the broader AIEH skills-based-hiring framework.

Who this guide is for

Candidates preparing for technical job searches at any seniority level.
Career-transition candidates translating prior experience into tech-role-relevant framing.
Working engineers updating their resume between job searches.

What resumes actually do

Three functions:

Pass automated screening. Many employers use ATS systems with keyword filters. Resumes that don’t match the job posting’s keywords get filtered before human review.
Pass initial human screening. Recruiters often spend 10-30 seconds per resume on first pass. The resume needs to communicate fit at scan-speed.
Provide context for interviewers. Once you’re scheduled for interviews, the resume informs interviewer expectations and behavioral-interview question selection.

The first function favors keyword-rich resumes; the second favors clear-structure resumes; the third favors specific-evidence resumes. The discipline is producing one resume that serves all three.

Strong resume patterns

Six patterns:

Lead with impact, not responsibility. “Reduced page-load p99 from 800ms to 120ms by replacing X with Y” is stronger than “Worked on frontend performance optimization.”
Quantify outcomes when honest. Specific metrics signal measurable impact; round numbers are usually fine. Don’t fabricate numbers — they get caught in interviews.
Match the job posting’s language. If the posting says “TypeScript” and you’ve used TypeScript, write “TypeScript” not “TS” or “JavaScript with type annotations.” ATS keyword matching is real.
Order by relevance, not chronology. Most-relevant experience near the top, even if it’s not your most recent role. Senior candidates with diverse backgrounds often need this.
Keep it scannable. Bullet points, consistent formatting, white space. One or two pages depending on seniority — three pages is rarely warranted.
Tailor per application or category. A frontend role resume should weight frontend experience differently than a backend resume; a research-leaning company should see research-leaning framing.

Common resume mistakes

Five patterns:

Vague accomplishment language. “Helped improve performance” is weaker than specific outcome claims.
Listing technologies without depth. A resume listing 30 technologies signals weak depth in any of them.
Inconsistent tense. Past roles use past tense; current role uses present tense. Mixing is jarring.
Including irrelevant experience. Senior candidates don’t need to include their early-career roles in detail.
Spelling or formatting errors. Resumes are pre- filtered; even small errors signal lack of care.

Portfolio considerations

Portfolios serve different purposes for different roles:

Engineers. GitHub portfolio with shipped projects; contributions to open-source signal community engagement; README quality of personal projects signals professional habits.
Designers. Figma or Behance portfolio with case studies; the case-study writeups matter as much as the visual outputs.
AI/ML practitioners. Kaggle competitions, published research, blog posts about projects, GitHub implementations.
Technical writers. Published documentation, blog posts, written work samples.

Strong portfolios are curated, not exhaustive. 3-5 strong case studies are stronger than 30 minor projects.

When AI assistance helps resume prep

Three patterns where AI is valuable:

Resume editing for clarity. AI is reliable at suggesting cuts and rephrases.
Bullet-point restructuring. Translating responsibility-language to impact-language.
Cover letter drafting. AI produces reasonable cover letter starting points; the writer refines.

Three patterns where AI is less valuable:

Genuine accomplishment claims. Specific outcomes have to come from real experience. AI can fabricate plausible-sounding accomplishments that don’t hold up in interviews.
Strategic framing. What experience to emphasize for a specific role requires understanding the role and your career narrative.
Cultural-fit calibration. Specific employers have cultural preferences; AI defaults often miss these.

How this maps to AIEH and the skills-based framework

Skills-based hiring (covered in the skills-based hiring evidence overview) is reducing reliance on resumes as primary signal, particularly with portable-credential approaches like AIEH’s Skills Passport. Resumes still matter for initial screening and human-review context, but their relative weight is declining as more direct skill signals become available.

Resources for deeper study

The Tech Resume Inside Out by Gergely Orosz. Practitioner-oriented book on tech-resume writing.
Career Cup, levels.fyi resume reviews for community-sourced resume feedback.
Specific employer resume guides when available (Google has published resume guidance, e.g.).

Common portfolio pitfalls

Including incomplete or broken projects. Portfolio pieces should work; broken demos signal incomplete follow-through.
Outdated projects. Last-updated-three-years-ago projects signal weaker recent activity.
No README or documentation. Strong projects include setup instructions and design decisions.

Takeaway

Resumes and portfolios serve multiple functions (ATS screening, human screening, interviewer context). Strong resumes lead with impact rather than responsibility, quantify outcomes honestly, match job-posting language, order by relevance, stay scannable, and tailor per application. Portfolios are curated case studies for roles where work-product evidence matters. AI assistance helps with editing but doesn’t substitute for genuine accomplishment claims or strategic framing.

For broader treatment of skills-based hiring and how portable credentials change the resume-and-portfolio landscape, see skills-based hiring evidence, skills vs credentials, and the scoring methodology.

Sources

Orosz, G. (2023). The Tech Resume Inside Out: A Practical Guide for Software Engineers. Self-published.
Stack Overflow. (2024). Stack Overflow Developer Survey 2024. https://survey.stackoverflow.co/2024/
HackerRank. (2024). Annual Developer Skills Survey. HackerRank. https://www.hackerrank.com/research/developer-skills/2024
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology. Psychological Bulletin, 124(2), 262–274.
Burning Glass Institute / Lightcast. (2022). The Emerging Degree Reset. https://www.burningglassinstitute.org/research