@DeRonin_: How to get a job as an AI Automation Engineer: AI automation is one of those fields where it's actually easier to get a…
Summary
Tips on breaking into AI automation engineering through non-traditional paths: build personal brand on X and GitHub, share knowledge in communities, specialize narrowly, and demonstrate measurable results.
View Cached Full Text
Cached at: 05/25/26, 10:35 AM
How to get a job as an AI Automation Engineer:
AI automation is one of those fields where it’s actually easier to get a job through non-traditional paths
companies need people who can wire AI into their existing tools, automate workflows, and save them 40+ hours a week
they don’t care where you learned it
so, here’s my workflow for getting a job as an AI Automation Engineer:
1: Build your personal brand on X
this should be your main platform
follow AI startup founders, reply to them, post your workflows, show your thinking and value
gonna to present next weeks how I’m doing it here
2: Build presence on LinkedIn or GitHub
these are more traditional platforms
but if you build your own automation system and it gets traction on GitHub, that’s already way stronger than “work experience at a company”
basically potential client checks out firstly your Github to get verification that you’re good at your deal
release open-source automation workflows to get the reputation
3: Share knowledge in communities
n8n community, LangChain Discord, OpenAI forums, Claude community
this is where real people hang out and opportunities appear
cheat codes to stand out and get into the top 1%:
1: Build in public
- show what you’re building and how
- CVs are outdated, people hire those who can build fast, ship real automations, and solve actual business problems
- Learn to build with AI agents, not just drag-and-drop
- one person with Claude Code can ship what used to take a 3-person dev team
- the no-code ceiling hits fast.. agents don’t have one
- Do free audits before calls
- before jumping on a call with a potential client, map out their broken workflows
- show what you’d fix and how (this can also be turned into content on X)
- Specialize narrowly
- don’t just be “AI automation engineer”
- pick a niche: real estate, e-commerce, recruiting, legal
- this makes you 10x easier to position and hire
- Show measurable results
- metrics matter (money especially)
- “saved 40 hours/week” and “$12K/mo in reduced headcount” is what gets you hired
- not “built a cool n8n workflow”
main insight:
this is a new profession, traditional “work experience” doesn’t matter as much
what matters is real skill, real projects, and proof you can ship
forget chasing FAANG interviews
right now you have a much better opportunity:
- build in public
- grow your brand
- become visible
- start earning within months, not years
one more thing: everything in this field moves insanely fast
what’s relevant today may be outdated in a year
so “experience” doesn’t matter
your real skill is adaptability + learning fast + constant practice
I wrote a 10,000+ word roadmap breaking down exactly what to learn each month for 6 months
every resource, every tool, every practice project
read it below
fact
and thinking why he is not getting everything in this life, he works 9-5 and has high degree lol
sheeesh, good job my fren, hope you loved it and it will bring to you right results
yep
currently building custom AI Automation solutions
for now just from my personal network
fact
Similar Articles
An article providing a step-by-step guide on becoming an AI engineer without a computer science degree, emphasizing building a portfolio of shipped projects over formal credentials.
An article providing a step-by-step guide on becoming an AI engineer without a computer science degree, emphasizing building a portfolio of shipped projects over formal credentials.
@DeRonin_: THIS IS HOW YOU WIN AS AN AI ENGINEER IN 2026: > ship one real app per month, ugly counts > master 4 things cold: promp…
A tweet from @DeRonin_ provides advice for AI engineers in 2026, emphasizing shipping real apps, mastering core skills, using cheap models, deploying widely, open-sourcing projects, and focusing on a single career lane.
@DeRonin_: As an AI engineer in 2026, learn this: > systematic output reading. pattern recognition across 1,000 model responses is…
A seasoned AI engineer shares key skills for 2026, including systematic output reading, context engineering, tool description discipline, eval design, model routing, prompt versioning, confidence scoring, streaming architecture, fallback chains, latency budgets, failure cataloguing, agent-vs-workflow decisions, and failure post-mortems as portfolio content.
@loganthorneloe: https://x.com/loganthorneloe/status/2074246640999731513
A guide on how to identify important topics to learn in AI by analyzing job listings and finding resources via RSS feeds and following experts. Emphasizes tracking in-demand skills and avoiding fads.
@cyrilXBT: https://x.com/cyrilXBT/status/2071604212912246899
A detailed guide on becoming an AI engineer in 2026 without a computer science degree, focusing on practical skills like integrating existing models and building pipelines, with a specific learning path.