What top companies want (2026)
A Java job posting says "5 years Spring, strong SQL" and mostly means it. An
"AI Engineer" posting in 2026 is noisier — RAG, agents, evals and inference optimization all
compete for the same title, and the loop that tests them differs by company. This page is
the sourced signal underneath the noise: two studies of real postings, and how five kinds of
companies actually run their loops.
sourced 2026-09-27
~10 min
2 studies, 4,600+ postings
5 loops
⚠️ Hiring facts change fast. Every figure
below is dated and sourced. Treat it as a starting point, not a guarantee — check the loop and
the bar with your own recruiter before an interview.
What top companies actually ask for
One analysis of 1,000+ AI-engineer job descriptions found 95.6% production-oriented
roles — most "AI engineer" postings want someone who ships, not someone who trains a model from
scratch. Python appeared in 82.5% of postings, AWS in 40%, model training in only 6.4%
(AI Shipping Labs).
A separate study of 3,647 postings at 198 AI companies — OpenAI, Anthropic, Databricks, Waymo
among them — found a sharper picture of which skills matter most
(aijobprep):
Share of 198 AI companies asking for each skill
👉 Every bar below is a real number from aijobprep's count, not an
estimate. Tap a skill for what it means in a posting and why it ranks where it does.
Tap a skill above.
Takeaways. Python and LLM fundamentals are close to universal (83%); agents,
observability and evals now outrank RAG on its own. A portfolio that only demonstrates RAG is
optimizing for the smallest of these seven bars.
How the big loops actually test you (2026)
The skills above tell you what to practice; the loops below tell you how each company checks
for it (Wrok,
Perspective,
Sundeep Teki).
Five loops, one tap each
👉 Tap a company or loop type; predict its distinguishing round before
you check.
Google
Hiring-committee packet review.
Amazon
Leadership Principles, every round.
Anthropic Applied AI
Five stages, customer discovery.
Forward-deployed (OpenAI/Anthropic/Palantir)
Scoping under constraints.
Tap a loop above.
Expert note. The GenAI system-design round (
IGotAnOffer)
follows a five-step framework — framing → architecture → deep dive → trade-offs → wrap-up —
and interviewers probe evals and regressions, token budgets and caching, prompt-injection
defence, chunking and embeddings, and rate limits and fallbacks. AI Architect and Solutions
Architect roles (AWS, Google Cloud, Microsoft) add enterprise requirements, cross-cloud
reference architectures and build-vs-buy, FinOps cost modelling, governance and compliance, and
presenting trade-offs to non-engineer stakeholders. The 12 worked designs and a timed-practice
mode for this framework are AJ3, planned next.
Check yourself