Module 1: AI Engineering Foundations and Systems Architecture
Learn how to design end-to-end AI architectures and translate technical and business requirements into scalable AI systems.
CAIEP®
Certified AI Engineering Professional
Engineer AI Systems. Deploy Intelligence. Build for Production.
Artificial Intelligence is moving rapidly from experimentation into production environments.
Engineer AI Systems That Run in Production.
Professional Level
For practitioners, specialists, analysts and engineers
Vendor-Neutral
Skills and knowledge that apply across platforms and tools
Global Recognition
Recognized internationally across industries and regions
Digital Credential
Shareable, verifiable credential and certificate
What You Will Learn
Master the core areas of ai engineering.
- 8 Comprehensive AI Engineering Modules
Module 2: Data Engineering and Feature Pipelines for AI
Build ingestion, cleaning, transformation, feature engineering, data-quality, lineage, and automated pipeline workflows.
Module 3: Machine Learning and Deep Learning Engineering
Develop, evaluate, optimize, package, and integrate machine learning and deep learning models.
Module 4: Generative AI, RAG, and AI Agent Engineering
Engineer LLM-powered applications, prompt workflows, RAG systems, vector search, AI agents, tool use, and multimodal solutions.
Module 5: AI Deployment, APIs, Cloud, and Infrastructure
Deploy models using APIs, containers, cloud services, batch and real-time inference, scalable infrastructure, and edge environments.
Module 6: MLOps, LLMOps, Monitoring, and Automation
Implement automated pipelines, model registries, versioning, observability, drift monitoring, prompt management, and lifecycle operations.
Module 7: AI Security, Responsible AI, and Engineering Risk
Secure AI systems against adversarial attacks, prompt injection, data leakage, API vulnerabilities, model exposure, and operational risk.
Module 8: Enterprise AI Integration, Reliability, and Professional Practice
Integrate AI with enterprise applications while addressing reliability, scalability, performance, cost, technical debt, and professional engineering practice.
Skills You Will Validate
By completing CAIEP®, candidates should be prepared to:
Design production-oriented AI architectures
Build AI data pipelines
Prepare and transform data
Engineer machine learning models
Integrate deep learning systems
Build LLM-powered applications
Develop RAG workflows
Engineer AI agents
Design model-serving APIs
Deploy AI applications
Containerize AI services
Support cloud AI environments
Implement MLOps
Apply LLMOps
Monitor models and applications
Detect drift and degradation
Secure AI systems
Improve reliability and scalability
Optimize AI performance and cost
Document engineering decisions
Why CAIEP® Stands Out
- AI Architecture
- Data Engineering
- Machine Learning Engineering
- Deep Learning
- Generative AI
- RAG
- AI Agents
- APIs
- Cloud Infrastructure
- MLOps
- LLMOps
- Cybersecurity
- Reliability Engineering
Career Opportunities
CAIEP® can support advancement toward roles such as:
- AI Engineer
- Machine Learning Engineer
- Generative AI Engineer
- AI Solutions Engineer
- AI Application Engineer
- MLOps Engineer
- LLMOps Engineer
- AI Platform Engineer
- AI Integration Engineer
- AI Systems Engineer
- Cloud AI Engineer
- AI Infrastructure Engineer
- AI Automation Engineer
- Data Engineer
- AI Developer
- AI Reliability Engineer
- AI Security Engineer
- AI Technical Consultant
- Enterprise AI Engineer
Actual role eligibility depends on professional experience, education, technical skills, and employer requirements.
View Career Outlook
About the credential
Become an AI Engineering professional the market trusts.
Organizations now need professionals who can do more than build models. They need engineers who can design AI architectures, manage data pipelines, integrate machine learning and Generative AI, deploy models securely, build APIs, implement MLOps and LLMOps, monitor performance, and maintain reliable AI systems at scale.
The Certified AI Engineering Professional (CAIEP®) is a professional technical certification designed to validate practical, vendor-neutral competency in the engineering, deployment, integration, security, and lifecycle management of Artificial Intelligence systems.
Offered by the: International Board of AI, Cybersecurity & Technology Professionals (IBACTP®)
CAIEP® validates the skills needed to transform AI models and prototypes into secure, scalable, production-ready enterprise solutions.
Professional level — Three-year certification cycle with continuing professional education
Build the Engineering Skills Behind Production AI
- AI Architecture
- Data Engineering
- Machine Learning
- Generative AI
- RAG
- AI Agents
- APIs
- Cloud
- MLOps
- LLMOps
- AI Security
- Reliability
Start Your CAIEP® Journey
- APPLY FOR CERTIFICATION
- REGISTER FOR THE CAIEP® EXAM
- ENROLL IN TRAINING
- CHOOSE THE CAPSTONE PATHWAY
- DOWNLOAD THE CERTIFICATION GUIDE
CAIEP®
Why Earn the CAIEP® Certification?
Building an AI model is only one part of delivering a successful AI solution.
Production AI requires professionals who understand how to connect data, models, applications, infrastructure, APIs, cloud platforms, monitoring, security, and operational processes.
CAIEP® is designed for professionals who want to demonstrate competency across the full AI engineering lifecycle.
The certification validates professional skills in:
- AI systems architecture
- Data engineering for AI
- Feature pipelines
- Machine learning engineering
- Deep learning engineering
- Generative AI application engineering
- Large Language Model integration
- Retrieval-Augmented Generation
- AI agents
- API development
- Cloud AI deployment
- Containers
- Model serving
- MLOps
- LLMOps
- Monitoring and observability
- AI security
- Responsible AI engineering
- Reliability and resilience
- Enterprise integration
CAIEP®
Who Should Earn CAIEP®?
CAIEP® is designed for professionals responsible for developing, integrating, deploying, or supporting AI-enabled systems.
Ideal candidates include:
- AI Engineers
- Machine Learning Engineers
- Generative AI Engineers
- AI Solutions Engineers
- AI Application Developers
- Software Engineers
- Data Engineers
- Data Scientists
- Cloud Engineers
- MLOps Engineers
- LLMOps Engineers
- DevOps Engineers
- DevSecOps Professionals
- Platform Engineers
- Systems Engineers
- Backend Developers
- API Developers
- Automation Engineers
- Cybersecurity Engineers
- Enterprise Architects
- Technical Consultants
- AI Implementation Specialists
- Technology Professionals transitioning into AI engineering
CAIEP®
CAIEP® Course Learning Outcomes
Upon successful completion of the Certified AI Engineering Professional (CAIEP®) course, participants will be able to:
-
01
Design AI Systems and Engineering Architectures
Translate technical and business requirements into end-to-end AI architectures that integrate data, models, APIs, applications, infrastructure, security, and monitoring.
-
02
Build Data and Feature Pipelines for AI
Prepare, transform, validate, and manage data used in AI systems while applying feature engineering, data-quality, lineage, versioning, and pipeline automation practices.
-
03
Develop and Integrate Machine Learning and Deep Learning Models
Select appropriate algorithms, train and evaluate models, optimize performance, package model artifacts, and integrate machine learning and deep learning into applications.
-
04
Engineer Generative AI, RAG, and AI Agent Solutions
Develop LLM-powered applications using prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation, AI agents, tool integration, and multimodal AI.
-
05
Deploy AI Applications, APIs, and Cloud-Based Services
Package and deploy AI systems using APIs, containers, model-serving environments, cloud infrastructure, batch and real-time inference, and scalable deployment patterns.
-
06
Apply MLOps, LLMOps, Monitoring, and Lifecycle Management
Implement model and prompt versioning, automated pipelines, model registries, observability, drift detection, retraining, release management, and continuous improvement.
-
07
Engineer Secure, Responsible, and Reliable AI Systems
Identify and mitigate AI security, privacy, adversarial, API, prompt, model, data, and operational risks while applying responsible AI and reliability principles.
-
08
Optimize and Integrate Enterprise AI Solutions
Evaluate performance, scalability, availability, cost, technical debt, maintainability, and enterprise integration requirements for production AI systems.
CAIEP®
CAIEP® Certification Testing Outcomes — Skills and Competencies Tested
The Certified AI Engineering Professional (CAIEP®) certification assessment is designed to evaluate applied technical knowledge, engineering judgment, system-design capability, troubleshooting skills, security awareness, deployment competency, and production AI engineering practices.
Candidates are expected to demonstrate competency across the following eight domains:
CAIEP®
The CAIEP®–IBACTP® AI Engineering Competency Model
The CAIEP®–IBACTP® AI Engineering Competency Model is built around eight integrated technical domains.
1. AI Architecture Competency
Design end-to-end AI solutions that integrate data, models, applications, APIs, infrastructure, security, and monitoring.
2. Data Engineering Competency
Build reliable ingestion, preprocessing, feature engineering, data-quality, lineage, and pipeline workflows.
3. Model Engineering Competency
Develop, evaluate, optimize, package, and integrate machine learning and deep learning models.
4. Generative AI Engineering Competency
Engineer LLM applications, prompt workflows, RAG systems, vector search, AI agents, and multimodal AI solutions.
5. Deployment and Infrastructure Competency
Deploy AI systems through APIs, containers, cloud environments, edge platforms, and scalable model-serving architectures.
6. AI Operations Competency
Apply MLOps, LLMOps, monitoring, observability, versioning, automation, retraining, and lifecycle management.
7. Secure and Responsible Engineering Competency
Identify and mitigate AI security, privacy, adversarial, data, API, prompt, model, and responsible AI risks.
8. Enterprise Reliability Competency
Engineer AI systems for reliability, maintainability, scalability, availability, cost efficiency, and enterprise integration.
CAIEP® Engineering Progression
Design → Build → Integrate → Deploy → Automate → Monitor → Secure → Optimize
CAIEP®
CAIEP® Production AI Engineering Capstone
Candidates selecting the applied pathway complete a structured:
CAIEP®
Production AI Engineering Capstone Project
The capstone evaluates the candidate's ability to engineer a complete AI solution.
CAIEP®
CAIEP® Certification Competency Standard
The CAIEP® assessment is designed to measure more than familiarity with AI engineering terminology.
Successful candidates should demonstrate the ability to apply technical knowledge and engineering judgment to realistic production AI scenarios.
The competency progression is:
Design → Build → Integrate → Deploy → Automate → Monitor → Secure → Optimize
Candidates may be evaluated through technical scenario-based examination questions, practical engineering exercises, or the CAIEP® Production AI Engineering Capstone Project.
The assessment should determine whether a candidate can make appropriate engineering decisions involving:
- AI architecture
- Data pipelines
- Machine learning models
- Deep learning
- Generative AI
- Retrieval-Augmented Generation
- AI agents
- APIs
- Cloud infrastructure
- MLOps
- LLMOps
- Cybersecurity
- Reliability
- Scalability
- Cost
- Enterprise integration
CAIEP®
What CAIEP® Validates
Earning the Certified AI Engineering Professional (CAIEP®) designation demonstrates professional competency in transforming AI concepts and models into secure, reliable, scalable, and production-ready systems.
CAIEP® validates the ability to:
This differentiates CAIEP® from broad AI practitioner credentials by emphasizing engineering implementation, deployment, operations, security, and production readiness.
- Design AI Architectures
- Build Data Pipelines
- Engineer Machine Learning Models
- Develop GenAI and RAG Applications
- Integrate AI Agents
- Deploy AI Services
- Implement MLOps and LLMOps
- Monitor Production AI
- Secure AI Systems
- Optimize Reliability, Scale, and Cost
CAIEP®
Hands-On Practical Labs
CAIEP® includes five integrated engineering labs designed to connect technical theory with real-world implementation.
Assessment
Flexible Certification Assessment Options
CAIEP® offers two ways to demonstrate competency.
CAIEP®
Recommended Training Duration · Certification Validity · Continuing Professional Education
Recommended Training Duration
CAIEP® is designed as a comprehensive:
Certification Validity
The recommended certification cycle is:
Continuing Professional Education
Recommended renewal requirement:
CAIEP®
40 CPE Credits Every Three Years
Qualifying professional development may include:
- AI engineering courses
- Machine learning training
- Generative AI development
- MLOps education
- LLMOps training
- Cloud architecture
- DevOps
- DevSecOps
- AI security training
- Professional conferences
- AI engineering projects
- Teaching
- Research
- Publications
- Advanced certifications
Where it leads
Professional Designation
Successful candidates earn the professional designation:
AI Engineering
ISO and International Framework Alignment
The CAIEP® Body of Knowledge is designed with consideration of recognized standards and frameworks relevant to AI, software engineering, cybersecurity, privacy, risk, and professional certification.
Relevant standards and frameworks include:
ISO/IEC 42001
Artificial Intelligence Management Systems.
Supports governance, accountability, AI risk management, lifecycle oversight, and continual improvement.
ISO/IEC 23894
Artificial Intelligence Risk Management.
Supports identification, evaluation, treatment, monitoring, and communication of AI risks.
ISO/IEC 22989
Artificial Intelligence Concepts and Terminology.
Provides standardized AI terminology and foundational concepts.
ISO/IEC 27001
Information Security Management Systems.
Supports secure engineering, access control, data protection, risk management, and incident response.
ISO/IEC 27701
Privacy Information Management.
Supports privacy requirements for AI systems handling personal or sensitive information.
ISO/IEC 25010
Systems and Software Quality Models.
Supports quality considerations involving reliability, performance, security, maintainability, usability, and compatibility.
ISO/IEC 12207
Software Lifecycle Processes.
Supports structured software and systems lifecycle practices applicable to AI engineering.
ISO 31000
Risk Management.
Supports structured engineering and operational risk management.
ISO/IEC 17024
Certification of Persons.
Provides internationally recognized principles for personnel certification.
NIST AI Risk Management Framework
Supports governance and management of AI-related risks.
CAIEP®
Global, Vendor-Neutral Focus
CAIEP® is designed as an internationally relevant and vendor-neutral AI engineering certification.
Its competencies are intended to transfer across:
This allows professionals to apply the CAIEP® Body of Knowledge across different technology ecosystems.
-
01
AI frameworks
Programming environments
-
02
Cloud providers
Model providers
-
03
Data platforms
Enterprise architectures
-
04
Countries
Industries
CAIEP®
Global Recognition and Professional Portability
The CAIEP® certification is designed to support professional mobility and demonstrate AI engineering competency across industries and geographic markets.
Relevant sectors include:
Recognition remains subject to the hiring, regulatory, educational, or professional requirements of individual organizations.
- Technology
- Financial Services
- Cybersecurity
- Healthcare
- Government
- Telecommunications
- Manufacturing
- Energy
- Education
- Retail
- Supply Chain
- Transportation
- Consulting
- Information Technology
- Information Systems
- Data and Analytics
- Professional Services
CAIEP®
Accreditation and Credentialing Alignment
CAIEP® is designed with consideration of professional personnel-certification and credentialing principles associated with:
The certification framework should incorporate:
Formal accreditation should only be claimed after officially awarded.
- ISO/IEC 17024
- ANSI National Accreditation Board (ANAB)
- National Commission for Certifying Agencies (NCCA)
- Institute for Credentialing Excellence (I.C.E.)
- International conformity-assessment practices
- Job Task Analysis
- Defined competency requirements
- Validated Body of Knowledge
- Examination blueprint
- Subject Matter Expert participation
- Psychometric review
- Candidate identity verification
- Examination security
- Impartial certification decisions
- Appeals and complaints
- Code of professional conduct
- Continuing Professional Education
- Recertification
- Credential verification
- Continuous improvement
CAIEP®
Your AI Engineering Certification Pathway
- Professional Level
CAIEP®
CAIP® vs. CAIEP®
| Area | CAIP® | CAIEP® |
|---|---|---|
| Focus | Broad AI professional competency | Technical AI engineering |
| Programming | Foundational | Important |
| Data Engineering | Awareness | Core |
| ML Development | Applied understanding | Engineering implementation |
| Generative AI | Professional use | Application engineering |
| RAG | Understanding | Design and implementation |
| AI Agents | Applied concepts | Engineering and integration |
| APIs | Awareness | Core |
| Cloud | General | Strong emphasis |
| MLOps | Understanding | Core competency |
| LLMOps | Understanding | Core competency |
| Security | Risk awareness | Secure engineering |
| Reliability | Limited | Core |
| Primary Outcome | AI professional | AI engineer |
CAIEP®
Choose Your Next Step
APPLY FOR CAIEP® CERTIFICATION
Ready to validate your AI engineering competency?
APPLY NOW →
REGISTER FOR THE CAIEP® EXAM
Already prepared?
Demonstrate your technical competency through the CAIEP® Certification Examination.
REGISTER FOR THE EXAM →
ENROLL IN CAIEP® TRAINING
Develop end-to-end AI engineering skills across all eight competency domains.
ENROLL NOW →
CHOOSE THE ENGINEERING CAPSTONE
Prefer applied assessment?
Demonstrate your engineering skills through the CAIEP® Production AI Engineering Capstone Project.
START YOUR CAPSTONE →
DOWNLOAD THE CAIEP® CERTIFICATION GUIDE
Review:
DOWNLOAD PROGRAM GUIDE →
- Eligibility
- Body of Knowledge
- Training structure
- Practical labs
- Assessment options
- Exam requirements
- Capstone requirements
- Renewal
- Professional designation
CAIEP®
Recommended Background
CAIEP® is a professional technical certification.
Candidates should preferably have familiarity with some of the following:
Advanced AI research experience is not required.
- Python or another programming language
- Databases
- APIs
- Data structures
- Machine learning fundamentals
- Cloud concepts
- Version control
- Software engineering
- Basic cybersecurity
- AI or data technologies
CAIEP®
Tools, Technologies, and Engineering Environments
CAIEP® is vendor-neutral but exposes candidates to the major categories of technologies used in modern AI engineering.
Depending on the approved training environment, candidates may gain experience with:
Programming and Development
- Python
- Jupyter
- Git
- SQL
- JSON
- REST APIs
- SDKs
- Command-line environments
Machine Learning
- scikit-learn
- TensorFlow
- PyTorch
- Classification
- Regression
- Clustering
- Feature engineering
- Model evaluation
- Experiment tracking
Generative AI
- Large Language Models
- Foundation models
- Prompt templates
- Embeddings
- Vector databases
- Retrieval-Augmented Generation
- AI agents
- Tool-enabled AI
- Multimodal models
Data Engineering
- Databases
- Data warehouses
- Data lakes
- ETL and ELT workflows
- Batch processing
- Streaming concepts
- Feature stores
- Data-quality controls
AI Deployment
- APIs
- Containers
- Container registries
- Model endpoints
- Cloud AI services
- Serverless concepts
- Edge AI
- Scalable inference
MLOps and LLMOps
- Model registries
- Version control
- Automated pipelines
- Experiment tracking
- Prompt versioning
- RAG monitoring
- Drift detection
- Observability
- Logging
- Evaluation pipelines
Infrastructure and Security
The certification focuses on transferable engineering practices rather than one specific platform or vendor.
- Cloud compute
- GPUs
- Storage
- Networking
- Identity and access management
- Secrets management
- Encryption
- API security
- Monitoring
- Incident response
CAIEP®
AI Engineering Applications
CAIEP® competencies are relevant to AI systems such as:
- Predictive analytics
- Fraud detection
- Recommendation systems
- Cybersecurity analytics
- Image recognition
- Computer vision systems
- NLP applications
- Intelligent document processing
- Generative AI assistants
- Enterprise copilots
- RAG knowledge assistants
- AI-powered search
- AI agents
- Workflow automation
- Intelligent customer support
- Predictive maintenance
- AI APIs
- Decision-support systems
- Cloud AI applications
- Edge AI
- Enterprise automation
CAIEP®
Secure AI Engineering Focus
AI engineering introduces new attack surfaces across models, APIs, data pipelines, cloud infrastructure, RAG systems, agents, and third-party dependencies.
CAIEP® addresses security risks such as:
Candidates learn to apply security thinking across the complete AI engineering lifecycle.
- Data poisoning
- Adversarial examples
- Model theft
- Model extraction
- Training-data leakage
- Prompt injection
- Indirect prompt injection
- Jailbreaking
- Sensitive information exposure
- API vulnerabilities
- Secrets exposure
- Excessive agency
- Unauthorized tool use
- Third-party AI risk
CAIEP®
Responsible AI Engineering
CAIEP® treats responsible AI as an engineering requirement.
Candidates learn to consider:
Responsible AI should be engineered into systems rather than treated as an afterthought.
- Fairness
- Bias
- Explainability
- Transparency
- Privacy
- Safety
- Human oversight
- Reliability
- Accountability
- Responsible deployment
- Data protection
- Monitoring
CAIEP®
Production AI and Reliability Focus
Production AI systems must remain dependable after deployment.
CAIEP® addresses:
- Availability
- Latency
- Throughput
- Model degradation
- Data drift
- Concept drift
- Fault tolerance
- Logging
- Observability
- Alerts
- Retraining
- Rollback
- Incident response
- Disaster recovery
- Model retirement
CAIEP®
Certified AI Engineering Professional (CAIEP®)
Active credential holders may use the designation after their names according to IBACTP® certification policies.
Example
Jane Smith, CAIEP®
The designation demonstrates professional competency in AI architecture, development, integration, deployment, MLOps, security, reliability, and enterprise AI engineering.
CAIEP®
CAIEP®
Certified AI Engineering Professional
Develop and validate professional competency in:
↓
- Architecture
- Data pipelines
- ML engineering
- Generative AI
- RAG
- AI agents
- APIs
- Cloud
- MLOps
- Security
- Reliability
- Advanced Manager Level
CAIEP®
One Credential. End-to-End AI Engineering Competency.
8 Technical Modules
From architecture through enterprise integration.
5 Practical Engineering Labs
Apply AI engineering in realistic technical environments.
2 Assessment Pathways
Certification Examination or Production Engineering Capstone.
1 Professional Designation
CAIEP® — Certified AI Engineering Professional
CAIEP®
Engineer AI That Works Beyond the Prototype. · Certified AI Engineering Professional (CAIEP®)
Engineer AI That Works Beyond the Prototype.
Design It. Build It. Integrate It. Deploy It. Secure It. Monitor It. Scale It.
Certified AI Engineering Professional (CAIEP®)
Offered by the International Board of AI, Cybersecurity & Technology Professionals (IBACTP®)
Design AI. Engineer Intelligence. Deploy Securely. Scale with Confidence.
The examination
Exam & Certification Details
Everything you need to plan your sitting.
CAIEP-100
Exam code for the Professional-level AI Engineering credential.
100 questions (maximum)
Multiple choice, completed in 120 minutes.
700 out of 1000
Passing score. Delivered in English.
Recommended experience
A minimum of two years of experience in ai engineering or a closely related technology discipline.
Where you sit it
IBACTP® approved testing centers and online proctored delivery
Staying certified
Three-year certification cycle with continuing professional education
Choose your route
Four ways to enroll. One credential.
Every route leads to the same CAIEP® examination and the same designation.
Progression
Your Certification Pathway
Start as a Professional. Advance as a Leader.
Get started
Ready to certify as a CAIEP®?
Self-Paced Learning
Exam fee only, with complimentary course materials provided — $400 USD.
Virtual Instructor-Led Training
4 days, 2 hours daily online. All course materials + Exam — $1,200 USD.
Bootcamps & Intensives
10 days, 2 hours daily. All course materials + Exam — $1,800 USD.
Corporate Training
Certify a whole team on a schedule that suits your organization. Fees negotiable.
Questions
Frequently Asked Questions — CAIEP®
What is CAIEP®?
CAIEP® is a professional AI engineering certification focused on designing, building, integrating, deploying, securing, monitoring, and maintaining production AI systems.
Who should take CAIEP®?
It is designed for AI engineers, software developers, data engineers, ML engineers, cloud professionals, MLOps professionals, systems engineers, and technical consultants.
Is programming required?
Basic programming knowledge is strongly recommended. Python is commonly used in AI engineering, although the certification remains vendor- and language-neutral.
Does CAIEP® cover Generative AI?
Yes. The program covers LLM integration, prompt engineering, RAG, vector databases, AI agents, multimodal AI, and LLMOps.
Does CAIEP® include machine learning?
Yes. Machine learning and deep learning engineering are major components.
Does CAIEP® cover RAG?
Yes. Candidates learn document ingestion, chunking, embeddings, vector retrieval, grounding, and RAG evaluation.
Are AI agents included?
Yes. Candidates study agent goals, tools, memory, workflows, orchestration, permissions, human approval, and security.
Does CAIEP® cover cloud deployment?
Yes. Cloud, containers, APIs, model serving, scalable infrastructure, and edge concepts are included.
Does CAIEP® cover MLOps?
Yes. MLOps is a core competency area.
Does it include AI security?
Yes. CAIEP® addresses model security, API security, adversarial AI, prompt injection, access control, privacy, and production security.
Is CAIEP® vendor-neutral?
Yes.
How long is training?
The recommended program is approximately 60 hours.
How is CAIEP® assessed?
Candidates may choose the Certification Examination or Production AI Engineering Capstone.
How long is the certification valid?
The recommended certification cycle is three years.
What certification comes after CAIEP®?
The advanced progression is:
Certified AI Engineering Manager (CAIEM®)
CAIEP®
Ready to Engineer the Future of AI?
Become CAIEP® Certified.
Organizations need professionals who can transform AI from an experiment into a dependable production capability.
CAIEP® is designed for professionals ready to:
- Design AI Systems
- Build Data Pipelines
- Engineer Models
- Integrate Generative AI
- Build RAG Applications
- Deploy AI Agents
- Operate MLOps
- Secure Production AI
- Scale Enterprise Solutions
Certified AI Engineering Professional (CAIEP®) · International Board of AI, Cybersecurity & Technology Professionals (IBACTP®)
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