2.1 AI Architect
Plans, designs and integrates the AI architecture.
Designs, integrates and implements AI solutions ensuring procedures and models for development are current and comply with common standards. Monitors new AI technology developments and applies if appropriate. Provides technological design leadership and guides the development of AI solutions.
- Competences and required levels
- A.1 e-4
- A.5 e-4
- A.7 e-4
- A.8 e-3
- B.2 e-4
- B.6 e-4
- E.3 e-3
- E.8 e-3
- Qualification level
- EQF 7 — Master's degree (graduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- AI Strategy, Operating Model Design for AI, AI Business Application & Impact, AI Governance, AI Ethics, AI Economics & Cost Management, AI System Architecture & Enterprise Integration, AI Solution Architecture, AI Infrastructure, AI Security, Data Engineering, AI Market & Technology Intelligence, Generative AI, AI Tooling & Ecosystem, AI Platforms & Frameworks, Safe, Responsible & Sustainable AI, Model Optimisation & Efficiency, AI Compliance & Regulatory Readiness, AI Risk, Assurance & Audit, Model Risk Management (MRM), Third-Party & Supply Chain AI Risk, AI Incident Management & Response
- Also advertised as
- AI Solutions Architect · Enterprise AI Architect · AI Infrastructure Engineer · AI Systems Architect · AI Infrastructure Architect · Machine Learning Architect · Generative AI Architect · Agentic AI System Architect · AI Application Architect · AI Software Architect · AI Cloud Architect
2.2 AI Product Designer
Designs the way how people experience AI-enabled products. They design interfaces, workflows, and interactions that make complex AI systems understandable, useful, and trustworthy with a focus on human needs, business goals, and machine capabilities.
Ensures AI products and services are intuitive, usable, and aligned with user and business needs.
- Competences and required levels
- A.4 e-3
- A.6 e-3
- A.9 e-3
- A.10 e-4
- B.5 e-3
- D.11 e-3
- E.4 e-3
- Qualification level
- EQF 7 — Master's degree (graduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- AI Product Management, AI Adoption & Enablement, Human-Model Interaction Design, MLOps & AI Lifecycle Management, AI Compliance & Regulatory Readiness, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Data Modelling, Explainability & Interpretability (XAI), Prompt Engineering, Generative AI, Synthetic Data Generation, Regulatory Reporting & Documentation, AI Business Application & Impact, AI Value Measurement & ROI, Vendor & Partner Management for AI
- Also advertised as
- AI Experience Designer · AI UX Designer · AI UX/Product Designer · Intelligent Product Designer · ML UX Designer · AI Interaction Designer · AI Systems Designer · Human-Centred AI Designer · Explainable AI Designer · XAI Designer · Responsible AI UX Designer · Conversational AI Designer · AI Service Designer · AI Design Lead · AI Design Specialist · Generative AI Designer · Human-AI Interaction Designer · Machine Learning UX Designer · Intelligent Systems Designer
2.3 AI Engineer
Builds and deploys AI models and systems.
Ensures building and implementing AI systems that may encompass a wide range of techniques. Ensures scalability, dependability and efficiency of AI systems. Manages deployment pipelines for AI solutions.
- Competences and required levels
- A.6 e-3
- A.7 e-3
- B.1 e-3
- B.2 e-3
- B.3 e-3
- B.4 e-3
- B.5 e-3
- C.4 e-3
- D.7 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Data Modelling, Explainability & Interpretability (XAI), Human-Model Interaction Design, Prompt Engineering, AI Market & Technology Intelligence, Generative AI, AI Tooling & Ecosystem, AI Platforms & Frameworks, Data Privacy Engineering, AI System Architecture & Enterprise Integration, Testing AI-based systems, Model Evaluation & Validation, MLOps & AI Lifecycle Management, AI Infrastructure, AI Security, Regulatory Reporting & Documentation, AI Incident Management & Response, Data Analytics, Data Engineering, AI Ethics
- Also advertised as
- AI Systems Engineer · AI Orchestration Engineer · AI Optimisation Engineer · AI Algorithm Engineer · Intelligent Systems Engineer · ML Engineer · ML Expert · DL Engineer · DL Expert · Neural Network Engineer · Computer Vision Engineer · Computer Vision Expert · NLP Engineer · NLP Expert · LLM Engineer · Generative AI Engineer · Conversational AI Engineer · Prompt Engineer · Agentic AI Engineer · AI Agent Engineer · Quantum AI Engineer · Quantum ML Engineer
2.4 AI Application Developer
Designs and develops AI applications to meet solution specifications.
Ensures developing, coding, and testing AI applications and algorithms. Writes code for AI models and develops AI algorithms. Implements AI solutions into applications and finetunes the AI models based on performance.
- Competences and required levels
- A.6 e-3
- B.1 e-3
- B.2 e-3
- B.3 e-2
- B.5 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Data Modelling, Explainability & Interpretability (XAI), Human-Model Interaction Design, Prompt Engineering, Generative AI, AI Platforms & Frameworks, Data Privacy Engineering, AI System Architecture & Enterprise Integration, AI Tooling & Ecosystem, Testing AI-based systems, Model Evaluation & Validation, Regulatory Reporting & Documentation
- Also advertised as
- AI Developer · AI Software Developer · AI Systems Developer · Intelligent Systems Developer · Business Intelligence Developer · AI Programmer · AI Practitioner · LLM Developer · AI Chatbot Developer · AI Agent Developer · Agentic AI Developer · Quantum AI Developer
2.5 AI Researcher
Conducts research, both, in AI and ML, to explore and develop new algorithms, models, and theoretical foundations in order to advance the field of AI.
Advances AI technology by creating novel methods, improving model capabilities, and contributing research that enables safe, ethical, and impactful AI systems for the benefit of society. Operates at the intersection of computer science, mathematics, and domain knowledge to push the boundaries of what machines can learn, reason, and do.
- Competences and required levels
- A.7 e-5
- A.9 e-5
- D.7 e-5
- D.10 e-4
- D.11 e-4
- Qualification level
- EQF 8 — Doctorate (graduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- AI Market & Technology Intelligence, Generative AI, AI Tooling & Ecosystem, AI Platforms & Frameworks, Machine Learning, Deep Learning, Synthetic Data Generation, AI Adoption & Enablement, Data Analytics, Data Engineering, AI Ethics, Data Quality & Governance, AI Compliance & Regulatory Readiness, AI Business Application & Impact, AI Value Measurement & ROI, Human-Model Interaction Design
- Also advertised as
- AI Research Scientist · ML Researcher · AI/ML/DL/LLM Scientist · AI Research Engineer · Algorithmic Research Engineer · Applied Research Scientist · Generative AI Research Scientist · AI Safety Research Scientist · Responsible AI Researcher · AI Ethics Research Scientist · Trustworthy AI Researcher
2.6 AI Quality & Evaluation Specialist
Ensures AI systems meet defined standards for performance, robustness, fairness, reliability, safety, and ethical behaviour prior to release. Evaluates AI systems across a wide range of tasks, use cases, and performance metrics. Assesses model outputs for accuracy, relevance, safety, robustness, and overall quality and reviews prompts, responses, and model behaviour against established standards.
Ensures high-quality, reliable, and responsible AI performance through end-to- end model evaluation. Designs and executes testing, validation, and evaluation processes that assess AI models and systems against technical, ethical, and business requirements.
- Competences and required levels
- A.8 e-3
- B.3 e-4
- B.5 e-3
- D.2 e-4
- D.7 e-2
- E.3 e-3
- E.6 e-3
- E.8 e-3
- Qualification level
- EQF 7 — Master's degree (graduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- Safe, Responsible & Sustainable AI, Model Optimisation & Efficiency, AI Compliance & Regulatory Readiness, Testing AI-based systems, Model Evaluation & Validation, Explainability & Interpretability (XAI), Regulatory Reporting & Documentation, AI Policy & Regulation, AI Risk, Assurance & Audit, Data Analytics, Machine Learning, Deep Learning, Data Engineering, AI Ethics, Model Risk Management (MRM), Third-Party & Supply Chain AI Risk, Data Quality & Governance, AI Security, AI Incident Management & Response
- Also advertised as
- AI Test Specialist · AI Tester · AI Testing Professional · AI Software Tester · Specialist Software AI/ML Tester · AI QA Tester · Tester AI Application Development · AI Test Engineer · AI Prompt Tester · AI Evaluator · Evaluator of AI Models · AI/ML Content Evaluator · AI Prompt Evaluator · AI Writing Evaluator · LLM Evaluator · AI Reviewer · AI Rater · AI Quality Rater · AI Data Rater · AI Quality Engineer · AI Quality Specialist · AI QA Specialist
2.7 AI Deployment Engineer
Packages, releases, and promotes AI models and AI-enabled services into production environments in a secure, scalable, and repeatable manner.
Ensures AI solutions move reliably from development to production by managing deployment pipelines, environment configurations, and release processes while minimising operational risk and downtime.
- Competences and required levels
- B.2 e-3
- B.3 e-2
- B.4 e-3
- B.5 e-2
- C.2 e-3
- C.3 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- AI System Architecture & Enterprise Integration, AI Tooling & Ecosystem, Testing AI-based systems, Model Evaluation & Validation, Explainability & Interpretability (XAI), MLOps & AI Lifecycle Management, AI Infrastructure, AI Security, Regulatory Reporting & Documentation, Change Management for AI, AI Performance Monitoring, AI Adoption & Enablement, Model Optimisation & Efficiency
- Also advertised as
- AI Implementation Engineer · AI Systems Engineer · AI Production Engineer · AI Integration Engineer · Model Deployment Engineer · ML Deployment Engineer · Applied AI Engineer
2.8 MLOps Engineer
Develops, implements and maintains processes and tools that automate creating, training, deploying and updating ML models.
Deploys and maintains the ML models in the production environment. Focuses on automating, simplifying, and streamlining ML models across the entire development lifecycle and improving workflows to develop, deploy, and manage them faster and easier. This includes standardising processes, increasing visibility and collaboration, and automating AI/MLOps tasks.
- Competences and required levels
- B.1 e-3
- B.2 e-3
- B.4 e-3
- B.6 e-3
- C.2 e-3
- C.3 e-3
- C.5 e-3
- E.5 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Provider, Operator
- Skill areas
- Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Generative AI, AI Platforms & Frameworks, Data Privacy Engineering, AI System Architecture & Enterprise Integration, AI Tooling & Ecosystem, MLOps & AI Lifecycle Management, AI Infrastructure, AI Security, Data Engineering, Change Management for AI, AI Performance Monitoring, AI Adoption & Enablement, Model Optimisation & Efficiency, Operating Model Design for AI, AI Automation
- Also advertised as
- AIOps Engineer · AIOps Specialist · AIOps Professional · AI Operations Specialist · MLOps Engineer · MLOps Specialist · MLOps Professional · ML Operations Specialist · LLMOps Engineer · AI/ML Workflow Engineer
2.9 AI Reliability Engineer
Ensures AI systems and pipelines are robust, resilient, and fault-tolerant. Designs architectures and processes that prevent failures, recover from incidents, and maintain operational stability, so AI systems can be trusted to run reliably at scale.
Delivers resilient and dependable AI systems by building fault-tolerant architectures, implementing safeguards, and optimising operational processes, ensuring AI performs reliably under all conditions.
- Competences and required levels
- A.5 e-3
- B.3 e-3
- C.5 e-3
- E.3 e-3
- E.6 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Deployer, Operator
- Skill areas
- AI System Architecture & Enterprise Integration, AI Solution Architecture, AI Infrastructure, AI Security, Data Engineering, Testing AI-based systems, Model Evaluation & Validation, Explainability & Interpretability (XAI), AI Performance Monitoring, AI Risk, Assurance & Audit, Model Risk Management (MRM), Third-Party & Supply Chain AI Risk, Data Quality & Governance
- Also advertised as
- ML Reliability Engineer · AI Ops Reliability Engineer · AI Robustness Engineer · AI Systems Reliability Engineer · AI Assurance Engineer · Trustworthy AI Engineer · Autonomous Systems Reliability Engineer
2.10 AI Observability & Monitoring Specialist
Ensures AI models and pipelines are continuously tracked, measured, and analysed. Detects anomalies, monitors model performance, and provides actionable insights to maintain accuracy, fairness, and trustworthiness in AI systems. Builds the instrumentation needed to observe AI systems and models in production.
Enables timely detection and diagnosis of issues by monitoring AI performance and drift, and by delivering actionable insights to support operational, technical, and business decision-making. Enables timely, informed responses that protect performance, compliance, and user trust.
- Competences and required levels
- B.3 e-3
- C.3 e-3
- C.4 e-3
- C.5 e-3
- D.7 e-3
- D.10 e-3
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Deployer, Operator
- Skill areas
- Testing AI-based systems, Model Evaluation & Validation, Explainability & Interpretability (XAI), AI Performance Monitoring, Model Optimisation & Efficiency, AI Infrastructure, AI Incident Management & Response, AI Security, Data Analytics, Machine Learning, Deep Learning, Data Engineering, AI Ethics, Data Quality & Governance, AI Compliance & Regulatory Readiness
- Also advertised as
- AI Monitoring Engineer · AI Observability Engineer · AI Performance & Monitoring Engineer · AI/ML Monitoring Specialist · ML Systems Monitoring Engineer
2.11 AI Incident Response & Reporting Lead
Oversees, coordinates and manages the identification, investigation, and resolution of AI system incidents while ensuring timely, accurate reporting to stakeholders and regulators. Collaborates across engineering, risk, and compliance teams to monitor AI behaviour, mitigate risks, and improve system safety and reliability.
Ensures safe, reliable, and accountable AI operations by leading incident response, analysing root causes, and providing clear, actionable reporting to stakeholders and regulators.
- Competences and required levels
- B.5 e-3
- C.3 e-3
- C.4 e-4
- E.3 e-3
- E.4 e-3
- E.8 e-4
- Qualification level
- EQF 7 — Master's degree (graduate)
- AI Act actor context
- Deployer, Operator
- Skill areas
- Regulatory Reporting & Documentation, Explainability & Interpretability (XAI), AI Performance Monitoring, Model Optimisation & Efficiency, AI Infrastructure, Model Evaluation & Validation, AI Incident Management & Response, AI Risk, Assurance & Audit, Model Risk Management (MRM), Third-Party & Supply Chain AI Risk, AI Adoption & Enablement, Vendor & Partner Management for AI, AI Security
- Also advertised as
- AI Incident Response Manager · AI Incident Lead · AI Systems Incident Manager · AI Safety Incident Coordinator · AI Operations Response Lead · AI Monitoring & Response Lead · Head of AI Incident Response & Reporting · AI Oversight & Incident Manager · AI Incident & Assurance Lead
2.12 AI Platform Engineer
Manages and maintains the shared infrastructure, platforms, and services that support the development, deployment, and operation of AI systems at scale.
Provides secure, scalable, and cost-efficient infrastructure and platforms by managing compute, storage, environments, and access controls that enable teams to build and run AI solutions effectively.
- Competences and required levels
- B.2 e-3
- B.3 e-2
- B.6 e-3
- C.3 e-3
- C.5 e-3
- E.3 e-3
- E.8 e-2
- Qualification level
- EQF 6 — Bachelor's degree (undergraduate)
- AI Act actor context
- Operator
- Skill areas
- AI System Architecture & Enterprise Integration, AI Tooling & Ecosystem, Testing AI-based systems, Model Evaluation & Validation, Explainability & Interpretability (XAI), AI Infrastructure, AI Platforms & Frameworks, AI Security, Data Engineering, AI Performance Monitoring, Model Optimisation & Efficiency, AI Risk, Assurance & Audit, Model Risk Management (MRM), Third-Party & Supply Chain AI Risk, AI Incident Management & Response
- Also advertised as
- AI Infrastructure Engineer · AI Systems Administrator · AI Admin · AI SysAdmin · ML Administrator · ML Admin