5.0 KiB
5.0 KiB
name, description
| name | description |
|---|---|
| artificial-intelligence-engineer | Occupational skill for the role 'artificial intelligence engineer' (also: intelligent systems designers, artificial intelligence designer, ICT intelligent systems designer, AI system designer, semantic technologies engineer, machine learning systems designer). Use when the user asks for typical artificial intelligence engineer work such as: Analyze, manipulate, or process large sets of data using statistical software.; Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.; Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods. |
Artificial Intelligence Engineer
Artificial intelligence engineers apply AI methods from engineering, robotics and computer science to design programs that simulate intelligence — including reasoning models, cognitive and knowledge-based systems, problem solving and decision making. They also integrate structured knowledge into computer systems (ontologies, knowledge bases) to solve complex problems that normally require a high level of human expertise, and they carry models from prototype to monitored production systems.
Core workflow
- Frame the problem: target metric, baseline, cost of errors, and whether AI is warranted at all (see references/intake.md — mandatory questions).
- Assess and prepare the data: sources, quality, labels, legal basis, leakage risks; build the evaluation set before the model.
- Choose the solution tier: rules/heuristic baseline → prompting an existing model → RAG over private data → fine-tuning → custom training.
- Build the pipeline: versioned data, features/prompts, training or orchestration code, reproducible experiments.
- Evaluate offline against the baseline — overall and per segment; for generative systems run the golden-example eval suite.
- Ship safely: shadow mode → canary → ramp-up, with rollback and guardrails (input validation, output schemas, human fallback).
- Monitor in production: drift, quality proxies, cost, latency; alert thresholds with owners.
- Iterate and govern: retraining triggers, model cards, bias reviews, compliance (e.g. EU AI Act risk class).
How to use this skill
- Read references/profile.md for the occupation profile and scope.
- Consult references/tasks.md for the full task and activity inventory.
- Check references/skills.md for essential vs. optional competences.
- Check references/tools.md for the software commonly used in this role.
- See references/ai-skills.md — matched external AI agent skills (tiered, per-source attribution).
- See references/market.md — FULL market-evidence report from real job ads (rankings, seniority, titles).
- See references/usecases.md — ranked work packages the agent can take over.
- See references/intake.md — mandatory questions before starting any task.
- See references/quality.md — acceptance criteria for deliverables.
- See references/glossary.md — definitions of the key tools and methods.
- See references/literature.md — standard works per top skill.
- See evals/ — test tasks with pass criteria.
- See references/practitioner-qa.md — curated Stack Exchange practitioner Q&A (per-entry attribution, CC-BY-SA 4.0).
Key competences (essential)
- algorithms
- analyse big data
- analyse business requirements
- apply ICT systems theory
- artificial neural networks
- business process modelling
- computer programming
- computer simulation
- create data sets
- creatively use digital technologies
- data mining
- data models
- data science
- define technical requirements
- deliver visual presentation of data
- design process
- develop creative ideas
- develop statistical software
- digital data processing
- information architecture
- information categorisation
- information extraction
- information structure
- principles of artificial intelligence
- Python (computer programming)
- resource description framework query language
- systems development life-cycle
- task algorithmisation
- unstructured data
- use data processing techniques
- visual presentation techniques
Hot technologies
Top tools from 78 gated job ads (see references/market.md, as of 2026-07-09):
- Python — 36 %
- Kubernetes — 21 %
- Docker — 19 %
- AWS — 18 %
- LangChain — 18 %
- Azure — 17 %
- PyTorch — 14 %
- TensorFlow — 13 %
- Git — 10 %
- GCP — 8 %
Sources: ESCO v1.2.1 (http://data.europa.eu/esco/occupation/35553663-deab-4d9a-bf22-15c1625d28e8), ONET 30.3 (15-2051.00). See manifest.json for licensing/attribution.* ONET nearest match: 15-2051.00 Data Scientists (proxy — no exact ONET occupation exists).