Two Resumes, No Lies

Categories: ai, career, engineering


Once the pipeline was finally stable, I hit the real question:

What do I want to say here, and how do I say it honestly?

1. The Temptation

When you’ve got AI helping out, it’s so easy to slip into things like:

  • Overstating experience
  • Inflating scope
  • Smoothing rough edges into fiction

Especially when it comes to AI.

2. The Constraint I Chose

My official title is Principal Software Engineer.

But titles aren’t what matters to me. They don’t capture the impact or the real work I put in.

Target:

  • Staff / Senior+ IC roles
  • Developer productivity teams
  • AI enablement spaces
  • Possibly education-focused AI

Constraint:

Everything I include has to be true, defensible, and based on actual, shipped work.

3. From Hype to Systems Thinking

This shift wasn’t about lowering my ambitions.

It meant thinking differently about impact:

  • Systems over features
  • Leverage over activity
  • Direction over claims

💬 Not:

“Built AI systems”

But:

“Improved systems where AI is becoming a multiplier”

4. Instead of One Resume… Two

I stopped chasing that one perfect version.

So I built two:

Conservative

  • Tight edits
  • Minimal expansion
  • Lower narrative risk

Aggressive

  • Stronger AI-forward framing
  • Clearer directional intent
  • Still grounded in the real work I’ve done

5. Why This Works

Because hiring isn’t binary.

Different companies optimize for different signals:

  • Some reward precision
  • Some reward trajectory
  • Some reward narrative clarity

So instead of guessing what would resonate…

I started testing positioning.

6. The Mission Section

I added a new section:

AI in education as augmentation, not replacement.

This does two things:

  1. Signals a commitment to long-term direction
  2. Anchors my AI work in responsibility

7. Working With AI Without Losing the Plot

The most helpful thing AI did wasn’t write for me.

It was:

  • Surfacing inconsistencies
  • Stress-testing claims
  • Generating structured alternatives

💡 AI is best used as a reviewer and amplifier
not a narrator of your experience

8. What Changed for Me

Before:

  • Resume = static artifact
  • Edit = one-shot decision

After:

  • Resume = system with variants
  • Edit = exploration space

Lessons Learned

  1. How you frame your story is just as important as the metrics you’ve achieved.
  2. Honesty outlasts hype, and real impact speaks for itself.
  3. Exploring multiple variants is more useful than chasing a single ‘perfect’ resume.
  4. AI works best as a collaborator, not a replacement.
  5. Where you’re headed matters as much as what you’ve done.

What I’d Do Next

  1. Add diff tooling between variants
  2. Build a lightweight “resume lint” system
  3. Create targeted versions for:
    • Dev productivity teams
    • Education AI companies
  4. Start publishing small, real AI projects

Closing Thought

The hardest part wasn’t fixing the pipeline.

It was figuring out what story to tell once it actually worked.

And making sure that story stayed true to what I’d actually accomplished.