# Maintenance and Repair Workers, General

> Perform work involving the skills of two or more maintenance or craft occupations to keep machines, mechanical equipment, or the structure of a building in repair. Duties may involve pipe fitting; HVAC maintenance; insulating; welding; machining; carpentry; repairing electrical or mechanical equipment; installing, aligning, and balancing new equipment; and repairing buildings, floors, or stairs.

- **SOC code:** 49-9071.00
- **Canonical URL:** https://singulariki.com/roles/role-49-9071-00
- **Also known as:** Facilities Technician, Maintenance Engineer, Maintenance Mechanic, Maintenance Technician, Building Mechanic, Equipment Engineering Technician, Maintenance Journeyman, Maintenance Man
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Perform routine maintenance, such as inspecting drives, motors, or belts, checking fluid levels, replacing filters, or doing other preventive maintenance actions.
- Inspect, operate, or test machinery or equipment to diagnose machine malfunctions.
- Adjust functional parts of devices or control instruments, using hand tools, levels, plumb bobs, or straightedges.
- Repair machines, equipment, or structures, using tools such as hammers, hoists, saws, drills, wrenches, or equipment such as precision measuring instruments or electrical or electronic testing devices.
- Order parts, supplies, or equipment from catalogs or suppliers.
- Perform routine maintenance on boilers, such as replacing burners or hoses, installing replacement parts, or reinforcing structural weaknesses to ensure optimal boiler efficiency.
- Diagnose mechanical problems and determine how to correct them, checking blueprints, repair manuals, or parts catalogs, as necessary.
- Design new equipment to aid in the repair or maintenance of machines, mechanical equipment, or building structures.
- Assemble, install, or repair wiring, electrical or electronic components, pipe systems, plumbing, machinery, or equipment.
- Clean or lubricate shafts, bearings, gears, or other parts of machinery.
- Provide groundskeeping services, such as landscaping or snow removal.
- Maintain or repair specialized equipment or machinery located in cafeterias, laundries, hospitals, stores, offices, or factories.

**Emerging tasks** (O*NET):
- Use drones for inspecting roofs, gutters, and other hard-to-reach areas of buildings.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mechanical _(knowledge)_
- Equipment Maintenance _(transferable_skill)_
- Repairing _(transferable_skill)_
- Information Ordering _(ability)_
- Arm-Hand Steadiness _(ability)_
- Manual Dexterity _(ability)_
- Near Vision _(ability)_
- Troubleshooting _(transferable_skill)_
- Problem Sensitivity _(ability)_
- English Language _(knowledge)_
- Building and Construction _(knowledge)_
- Oral Expression _(ability)_

**Skills in demand:**
- Information Ordering _(Specialized Skill)_
- Equipment Maintenance _(Specialized Skill)_
- English Language _(Common Skill)_
- Visualization _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Active Learning _(Common Skill)_

**Tools & technology:**
- Microsoft Office software _(hot technology, in demand)_
- Apple macOS _(hot technology)_
- Autodesk AutoCAD _(hot technology)_
- Facebook _(hot technology)_
- Google Docs _(hot technology)_
- Linux _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Project _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 20th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 17th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 28th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 21st percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 54th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.8% growth (About average); 159.8k annual openings; 1,629.7k → 1,692.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $48,620; 1,531,700 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 22% automation, 41% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** learning.

**Tasks most handed to AI here:**
- Diagnose mechanical problems and determine how to correct them, checking blueprints, repair manuals, or parts catalogs, as necessary. _(0.6% of measured AI use; feedback loop)_
- Test and treat water supply. _(0.4% of measured AI use; learning)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me diagnose mechanical problems and determine how to correct them, checking blueprints, repair manuals, or parts catalogs, as necessary.
- Help me test and treat water supply.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-49-9071-00_