# Radio Frequency Identification Device Specialists

> Design and implement radio frequency identification device (RFID) systems used to track shipments or goods.

- **SOC code:** 17-2072.01
- **Canonical URL:** https://singulariki.com/roles/role-17-2072-01
- **Also known as:** Deployment Engineer, RFID Engineer (Radio Frequency Identification Device Engineer), RFID Systems Engineer (Radio Frequency Identification Device Systems Engineer), Technical Support Engineer, Electro Magnetic Compatibility Test Engineer, Antenna Engineer, Cardiac Device Specialist, DSP Engineer (Digital Signal Processing Engineer)
- **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):
- Identify operational requirements for new systems to inform selection of technological solutions.
- Integrate tags, readers, or software in radio frequency identification device (RFID) designs.
- Perform systems analysis or programming of radio frequency identification device (RFID) technology.
- Test radio frequency identification device (RFID) software to ensure proper functioning.
- Select appropriate radio frequency identification device (RFID) tags and determine placement locations.
- Perform site analyses to determine system configurations, processes to be impacted, or on-site obstacles to technology implementation.
- Perform acceptance testing on newly installed or updated systems.
- Determine means of integrating radio frequency identification device (RFID) into other applications.
- Provide technical support for radio frequency identification device (RFID) technology.
- Collect data about existing client hardware, software, networking, or key business processes to inform implementation of radio frequency identification device (RFID) technology.
- Test tags or labels to ensure readability.
- Install, test, or maintain radio frequency identification device (RFID) systems.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Computers and Electronics _(knowledge)_
- Engineering and Technology _(knowledge)_
- English Language _(knowledge)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Deductive Reasoning _(ability)_
- Critical Thinking _(essential_skill)_
- Written Comprehension _(ability)_
- Inductive Reasoning _(ability)_
- Active Listening _(essential_skill)_
- Speaking _(essential_skill)_
- Oral Expression _(ability)_

**Skills in demand:**
- English Language _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Active Listening _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Mathematics _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Telecommunications _(Specialized Skill)_
- Complex Problem Solving _(Common Skill)_

**Tools & technology:**
- C _(hot technology, in demand)_
- C++ _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- The MathWorks MATLAB _(hot technology, in demand)_
- C# _(hot technology)_
- Dassault Systemes SolidWorks _(hot technology)_
- ESRI ArcGIS software _(hot technology)_
- Extensible markup language XML _(hot technology)_
- JUnit _(hot technology)_
- Linux _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 76th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 63rd percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 79th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 84th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 16th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 6.2% growth (About average); 5.7k annual openings; 95.9k → 101.8k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $127,590; 93,940 employed.

## How people actually use AI here

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

- **Automation vs augmentation:** 14% automation, 19% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** learning.

**Tasks most handed to AI here:**
- Define and compare possible radio frequency identification device (RFID) solutions to inform selection for specific projects. _(0.4% of measured AI use; directive)_
- Provide technical support for radio frequency identification device (RFID) technology. _(0.4% of measured AI use; learning)_
- Collect data about existing client hardware, software, networking, or key business processes to inform implementation of radio frequency identification device (RFID) technology. _(0.3% of measured AI use)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me define and compare possible radio frequency identification device (RFID) solutions to inform selection for specific projects.
- Help me provide technical support for radio frequency identification device (RFID) technology.
- Help me collect data about existing client hardware, software, networking, or key business processes to inform implementation of radio frequency identification device (RFID) technology.

## 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-17-2072-01_