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// ORIGINAL DATA · v1

The State of Agent Engineering Hiring, 2026

I pulled 119 agent/AI-engineer job postings from 32 employers using only public ATS job-board APIs (Greenhouse, Lever, Ashby), coded each one against a fixed rubric, and asked a simple question: which production, agent and evaluation responsibilities are named? This is a companion to the State of FDE study and a separate corpus from it. The aggregates describe the published July snapshot, not current vacancies. The dataset and methodology are free.

By Adam Boudjemaa · Snapshot 2026-07-24 · CC BY 4.0 · Extracted facts + link-backs only, no verbatim postings

Preliminary sample. These 119 postings come from a selected set of employers, not a representative survey of the market. Full caveats in Limitations.

Headline findings

81.5%

of sampled agent/AI-engineer postings mention a framework or agentic pattern

97 of 119 name LangChain, LangGraph, CrewAI, AutoGen, tool use, function calling, or multi-agent orchestration. This is a keyword signal in this sample, not a complete definition of the role.

89.1%

require production coding

106 of 119 match the production-coding rubric. Verify actual responsibilities in the interview.

35.3%

mention evals, red-teaming, or guardrails as part of the role

42 of 119. These terms are present in the posting text; their absence does not establish that a team skips evaluation.

16%

of postings are based in France or elsewhere in the EU

19 of 119. Small enough that this is reported as one cut, not split further by country.

What these roles are called

"AI Engineer" is the single largest bucket at 42.9% (51 of 119postings), but a third of the corpus ("Other") uses a title outside the five target families entirely. The label hasn't settled yet.

AI Engineer42.9% (51)
Other34.5% (41)
Applied AI Engineer16% (19)
Agent Engineer4.2% (5)
Forward-Deployed AI Engineer2.5% (3)

Agent-related signals in the posting text

Job titles drift faster than job content. One useful signal is whether the posting names an agent framework or agentic pattern.

Agent framework / agentic pattern named81.5%
Production coding required89.1%
Evals / red-teaming / guardrails mentioned35.3%
Autonomy / human-in-the-loop mentioned37.8%
INCLUSION GATE
Decide: How is the posting included and coded?Decision tree: How is the posting included and coded? If The title is in the agent family and the posting names an agent framework or agentic pattern, then Counted, with the agent signal set. If The title is in the agent family but no framework or pattern is named, then Counted, with no agent signal detected. If The title is plain Machine Learning Engineer or Data Scientist, then Excluded by the title gate. If The source is LinkedIn, Indeed, Wellfound, YC or Google, then Never collected.DECIDEHow is the posting includedand coded?If the title is in theagent family and theposting names an agentframework or agenticpatternCounted, with theagent signal setLangChain, LangGraph,CrewAI, AutoGen, tool use,function calling, ormulti-agent orchestration.81.5% of the set.If the title is in theagent family but noframework or pattern isnamedCounted, with noagent signal detectedKept in the corpus so the81.5% has an honestdenominator rather than ahand-picked one.If the title is plainMachine Learning Engineeror Data ScientistExcluded by the titlegateDeliberately narrower thanML hiring, which is adifferent and much largermarket.If the source is LinkedIn,Indeed, Wellfound, YC orGoogleNever collectedOutside this study’scollection scope. Onlypublic Greenhouse, Leverand Ashby boards were used.
Title matching selected the sample. A separate keyword check recorded whether a framework or agentic pattern was mentioned.

What they require

LLM STACK MENTIONS

RAG42.9%
OpenAI API22.7%
Anthropic / Claude21.8%
Vector DB (Pinecone, Weaviate, etc.)18.5%

TOP LANGUAGES

Python58%
TypeScript / JS29.4%
Go21.8%
SQL13.4%
C++4.2%
Java3.4%

58%

remote

31.9%

onsite

10.1%

hybrid

Experience floor where stated (n=82): median 5 years, p25 3, p75 6.

The France / EU cut

19 of 119 postings (16%) are based in France or elsewhere in the EU. The sample is too small to split further by country without implying precision it doesn't have, so it's reported as one cut.

Context: the demand signal is not just this dataset

This isn't Adam's data, and it isn't computed from the fact table above. It's cited, not asserted: Skills related to agentic AI (AI agents, agentic systems) grew from 0.06% of US job postings in 2024 to 0.23% in 2025. That is a year-over-year increase of more than 280%, about 90,000 postings. Lightcast, “Four Takeaways from the 2026 Stanford AI Index,” 2026.

Get the data

COLLECTION PIPELINE
Flow diagram: 6 stepsFlow diagram: 6 steps. Seed list, then Public ATS APIs only. Public ATS APIs only, then Title gate. Title gate, then Extract structured fields. Extract structured fields, then Keyword coding rubric. Keyword coding rubric, then 119-row fact table, 32 employers.1Seed list~170 mostly US,venture-backed, AI-native andinfra companies, curated byhand.2Public ATS APIs onlyGreenhouse, Lever and Ashby.Other platforms are outsidethe collection scope.3Title gateApplied AI Engineer, AgentEngineer, AI Engineer,Forward-Deployed AI Engineer,evals and AI-safetyspecialists.4Extract structured fieldsFacts and link-backs only. Noverbatim posting prose isstored or republished.5Keyword coding rubricDerived signals recordkeyword matches for agentframeworks, evaluations andautonomy. Raw text andextraction code are notincluded in this download.6119-row fact table, 32employersPublished as JSON under CC BY4.0. Never blended with theseparate FDE corpus at/state-of-fde.
Study figures come from the 119-row table. The external context section is separately sourced; coding can produce false positives and false negatives.
DATASET SCHEMA
postings[]: one row per job posting
employerstring

Hiring company, plus its board slug and ATS platform.

posting_id / posting_urlstring

Stable ID and a link back to the original posting.

collected_atdate

Snapshot date. This is open roles at a point in time, not hires.

title_raw / terminologystring

The title as posted, and the normalised family it was mapped to.

location_raw / regionstring

Location as posted, and the coarse region bucket used for the EU cut.

remote_hybrid_onsiteenum

Work model, as stated by the posting.

yoe_minnumber | null

Minimum years of experience, only when the posting states one.

languages / cloud / llm_stack_mentionsstring[]

Technologies named in the posting.

agent_framework_mentionedboolean

The load-bearing signal: does it name an agent framework or agentic pattern?

evals_or_redteam_mentionedboolean

Evaluation, red-teaming or guardrails named as a responsibility.

autonomy_or_hitl_mentionedboolean

Autonomy or human-in-the-loop named.

production_coding_signalboolean

Whether the posting matches the study’s production-coding signal.

One row per posting in dataset-v1.json. Derived boolean signals are the coded fields; everything else is captured as collected.

Dataset (JSON)

All 119 rows, extracted fields + link-backs. CC BY 4.0.

Methodology

Sources, seed list, inclusion rules, coding rubric, limitations.

Limitations (read these)

  • Sampling frame: public Greenhouse/Lever/Ashby boards only, from a hand-curated seed list of about 170 mostly US, venture-backed, AI-native/infra companies. This is agent-engineering hiring among that population, not the whole market. It under-covers large enterprise on private ATS, APAC, and companies not on these three ATS platforms.
  • Preliminary convenience sample: 119 postings across 32 employers. Percentages describe these collected rows; they are not representative estimates of the whole hiring market.
  • Snapshot, not flow: point-in-time open roles are not hires. Trend claims need a second edition.
  • Title-gate precision: “AI Engineer” is a broad, fast-drifting title. The gate is deliberately narrower than plain “Machine Learning Engineer” to stay on the agent-engineering family this study targets, which means some genuinely agent-adjacent roles with unusual titles are excluded.
  • Derived fields (agent framework, evals, autonomy signals) are coded by a keyword rubric applied to posting text; not human-adjudicated for every row.
  • Never blended with the FDE study. The 519-posting dataset at /state-of-fde is a different corpus (forward-deployed-engineer title family); no row here is drawn from or merged with it.

Cite this

Boudjemaa, Adam. "State of Agent Engineering Hiring 2026," v1. adam-boudjemaa.com, 2026-07-24. https://adam-boudjemaa.com/state-of-agent-engineering. Data licensed CC BY 4.0.

▸ THE STATE OF FDE STUDYWHAT IS AN FDE?