Automotive Engineering Technicians
Architecture and Engineering Occupations- O*NET-SOC कोड
- 17-3027.01
Assist engineers in determining the practicality of proposed product design changes and plan and carry out tests on experimental test devices or equipment for performance, durability, or efficiency.
पेशों के नाम और कार्य-विवरण प्रकाशित रूप में अंग्रेज़ी में दिखाए जाते हैं। लेबल, जिनमें कार्य-प्रकार भी शामिल हैं, अनूदित हैं।
AI एक्सपोजर
- डेटा स्रोत: BLSप्रकाशन: 2026-08
उच्च· सापेक्ष
कमचार सापेक्ष श्रेणियाँबहुत उच्चव्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
चार सापेक्ष बैंड (कम / मध्यम / उच्च / बहुत उच्च)
BLS रोज़गार पूर्वानुमान तालिका के 831 विस्तृत व्यवसायों के आधार पर। मान NEM (नेशनल एम्प्लॉयमेंट मैट्रिक्स) कोड के स्तर पर दिया जाता है, इसलिए एक ही NEM कोड वाले व्यवसायों को वही बैंड मिलता है
- डेटा स्रोत: Anthropicप्रकाशन: 2026-03
0.068
0.000यहाँ दिए गए मानों की सीमा0.745व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
प्रेक्षित एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
O*NET कार्यों पर मैप किया गया
SOC 2018 व्यवसाय के स्तर पर प्रकाशित; समान SOC 2018 कोड वाले हर O*NET व्यवसाय को यही मान मिलता है
- डेटा स्रोत: ILOप्रकाशन: 2025
0.26
0.09यहाँ दिए गए मानों की सीमा0.70व्यवसाय-समूह स्तर का मान
मापक्रम, आधार और स्रोत
जेनरेटिव AI एक्सपोजर सूचकांक, प्रकाशित रूप में 0–1
ISCO-08 इकाई समूह के स्तर पर प्रकाशित मान। अमेरिकी श्रम सांख्यिकी ब्यूरो (BLS) की आधिकारिक क्रॉसवॉक तालिकाओं (ISCO-08 से 2010 SOC, 2010 SOC से 2018 SOC) को प्रकाशित रूप में लागू करके इस व्यवसाय से जोड़ा गया है; यह मिलान पूर्ण या आंशिक है
यह स्रोत किस प्रकार का आँकड़ा प्रकाशित करता है
BLS की श्रेणी सापेक्ष रैंक है, निरपेक्ष स्तर नहीं, और यह प्रथम-हस्त माप भी नहीं है: यह कई प्रकाशित अध्ययनों में व्यवसाय की पर्सेंटाइल रैंकों को चार बैंडों में बांटती है। यह रोज़गार या वेतन का पूर्वानुमान नहीं है, न अपनाए जाने की संभावना, और यह स्वचालन और संवर्धन में अंतर नहीं करती।
AI एक्सपोज़र (OpenAI रूब्रिक)
18 मूल्यांकित कार्य · β ≥ 0.5 वाले 9 कार्य (50.0%)
β = प्रत्यक्ष एक्सपोजर (E1) + 0.5 × उपकरण उपलब्ध होने पर एक्सपोजर (E2), स्रोत रिपॉजिटरी की परिभाषा के अनुसार।
- स्रोत इकाई: O*NET 27.2 के कार्य → O*NET 31.0 का पेशा कोड
- सभी मूल्यांकित कार्य O*NET 31.0 की कार्य-सूची में हैं।
- स्रोत
- OpenAI "GPTs are GPTs" exposure rubric
- संस्करण
- gh-main-0471612
- लाइसेंस
- MIT License, Copyright (c) 2024 OpenAI
कार्य
O*NET® 31.0 Database के कार्य-विवरण, मुख्य कार्य पहले।
| कार्य | प्रकार | β (OpenAI) |
|---|---|---|
| Build instrumentation or laboratory test equipment for special purposes. | मुख्य | 0 |
| Order new test equipment, supplies, or replacement parts. | मुख्य | 0.5 |
| Set up mechanical, hydraulic, or electric test equipment in accordance with engineering specifications, standards, or test procedures. | मुख्य | 0 |
| Recommend tests or testing conditions in accordance with designs, customer requirements, or industry standards to ensure test validity. | मुख्य | 0.5 |
| Monitor computer-controlled test equipment, according to written or verbal instructions. | मुख्य | 1 |
| Read and interpret blueprints, schematics, work specifications, drawings, or charts. | मुख्य | 0.5 |
| Maintain test equipment in operational condition by performing routine maintenance or making minor repairs or adjustments as needed. | मुख्य | 0 |
| Install equipment, such as instrumentation, test equipment, engines, or aftermarket products, to ensure proper interfaces. | मुख्य | 0 |
| Inspect or test parts to determine nature or cause of defects or malfunctions. | मुख्य | 0 |
| Document test results, using cameras, spreadsheets, documents, or other tools. | मुख्य | 0.5 |
| Fabricate new or modify existing prototype components or fixtures. | मुख्य | 0 |
| Analyze test data for automotive systems, subsystems, or component parts. | मुख्य | 0.5 |
| Recommend product or component design improvements, based on test data or observations. | मुख्य | 0.5 |
| Perform or execute manual or automated tests of automotive system or component performance, efficiency, or durability. | मुख्य | 0 |
| Analyze performance of vehicles or components that have been redesigned to increase fuel efficiency, such as camless or dual-clutch engines or alternative types of air-conditioning systems. | मुख्य | 0.5 |
| Improve fuel efficiency by testing vehicles or components that use lighter materials, such as aluminum, magnesium alloy, or plastic. | मुख्य | 0 |
| Participate in research or testing of computerized automotive applications, such as telemetrics, intelligent transportation systems, artificial intelligence, or automatic control. | मुख्य | 0.5 |
| Test performance of vehicles that use alternative fuels, such as alcohol blends, natural gas, liquefied petroleum gas, biodiesel, nano diesel, or alternative power methods, such as solar energy or hydrogen fuel cells. | मुख्य | 0 |
व्यावसायिक जानकारी
स्रोत और श्रेय
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 17-3027.01 Automotive Engineering Technicians
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.