Certified AI Engineer (CAIE)™
The exam validates a candidate’s ability to apply AI engineering foundations, data engineering, model development, deployment/MLOps, governance, and applied use cases to real-world scenarios. It is designed for professionals who have completed the GIofAI AI Engineering learning pathway or have equivalent industry experience.
Self-paced Course
Learn with labs & projects at your own pace.
Practice Exam
Timed questions with instant feedback & review.
AI systems are shipping faster—engineering rigor must keep up.
As AI moves from prototypes to production, teams need repeatable practices across data, model training, deployment, monitoring, and governance. CAIE focuses on the engineering realities that determine whether AI succeeds in the real world.
From experiments to systems
Production AI demands reproducibility: versioned data, tracked experiments, validated deployments, and measurable outcomes—beyond a notebook that “works once.”
Monitoring is now mandatory
Drift, data quality changes, and shifting user behavior can quietly break models. Engineers need robust observability, alerting, and retraining strategies.
Governance meets engineering
High-impact AI requires security controls, documentation, audits, and responsible practices—implemented as engineering workflows, not last-minute checklists.
Exam Curriculum
The CRAIS certification covers a comprehensive curriculum designed to equip professionals with the knowledge and skills to navigate the complex landscape of responsible AI.
Foundations & Data for AI Engineering
AI vs ML vs DL, lifecycle, and engineering roles Core math/stat: optimization, loss, regularization Framework concepts: PyTorch, TensorFlow, JAX, ONNX Reproducibility: versioned artifacts and configs
Model Development & Training
Classical ML: trees, ensembles, clustering, PCA DL: CNNs, RNNs, Transformers, hybrid approaches GenAI: VAEs, GANs, diffusion, LLM concepts Tuning: optimizers, schedules, AutoML approaches
Deployment, MLOps & AI Infrastructure
Batch vs real-time inference; edge vs cloud/hybrid Serving: REST/gRPC, containers, serverless concepts Enterprise integration: APIs, microservices, vector DBs RAG pipeline components and latency trade-offs
Evaluation & Monitoring
Metrics: classification, regression, NLP, vision Robustness: stress tests, shifts, reproducibility Monitoring: drift, dashboards, logging, alerting Online evaluation: A/B tests and thresholding
AI Governance, Security & Compliance
Risk frameworks: NIST AI RMF, ISO/IEC 42001 (conceptual) FATE: fairness, accountability, transparency, explainability Security: poisoning, inversion, prompt injection, API hardening Compliance concepts: GDPR, HIPAA, EU AI Act
Applied AI Engineering Case Studies
Retail: recommendations, vector search, bias trade-offs Finance: fraud, explainability, high-risk governance Healthcare: safety, human-in-the-loop, PHI constraints Manufacturing & public sector: edge, fairness, accountability
Syllabus Weightage
Sample Examination
Experience the scenario-based methodology used in GIofAI professional assessments.
Tip: GIofAI scenario questions often present multiple technically "valid" options. You must choose the one that aligns best with the FATE (Fairness, Accountability, Transparency, and Explainability) framework and institutional risk policy.
GIofAI Exam Portal
A world-class testing interface designed for precision, security, and accessibility.
Clean, Distraction-Free UI
Our portal uses a high-contrast, minimalist design to help you focus on complex case studies.
Integrated Utility Tools
Access on-screen scientific calculators, fairness metric scratchpads, and translation assistants.
Safe Exam Browser
Institutional-grade lockdown technology ensures the integrity of your professional designation.
Flexible Session Management
Auto-save features and interruption-recovery protocols protect your progress against connectivity drops.
Logistics & Compliance
Global delivery with institutional security standards.
Exam Format
Retake Policy
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Up to 2 retakes permitted within a 6-month period.
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Each retake requires a separate examination purchase.
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Practice Exam completion is strongly recommended prior to retake attempts.
Scoring & Results
No Negative Marking
Candidates are encouraged to answer all 250 questions; there is no penalty for incorrect answers.
Instant Provisionals
View your provisional result immediately upon submitting your examination session.
Official Transcript
Verified certificate and transcript will be available in your dashboard within 48 hours.
Verified Identity
Biometric and government ID verification is mandatory for all candidates via the web-based portal.
Transcript & Certificate
Official documentation of your certification achievement.
Official Transcript
Your verified transcript includes detailed performance metrics and section-wise scores.
Digital Certificate
Your professional certification credential, verifiable and shareable.
Frequently Asked Questions
Details for candidates and teams evaluating the credential.
CAIE is built for professionals working or aspiring to work as AI Engineers, ML Engineers, MLOps Engineers, or AI-focused Data Engineers—especially those building production systems.
The assessment is online and remotely proctored, timed at 120 minutes, and uses single-correct MCQs including scenario-based items that mirror real AI engineering decisions.
No. The assessment is closed-book with no external assistance, to preserve the integrity of the credential.
The highest weight is on Model Development & Training, plus a strong focus on Foundations & Data Engineering and Deployment/MLOps. Evaluation, monitoring, governance, and applied case studies complete the blueprint.
You receive a certificate-style credential with an ID and issue date, plus a transcript-style view that can summarize performance signals for verification workflows.
Prove you can ship AI systems—reliably, responsibly, and at scale.
CAIE validates engineering-grade competence across data pipelines, model development, deployment/MLOps, monitoring, and governance—through a structured, scenario-led assessment experience.
Career Path
A progression from entry to leadership, aligned with certifications.
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Entry-Level Roles
- AI Practitioner
- Junior AI Engineer
- Data Analyst / AI Technician
Advanced Roles
- AI Consultant (Finance, Healthcare, Manufacturing, etc.)
- AI Solutions Architect
- AI Research Engineer
- AI Security & Privacy Specialist
Intermediate Roles
- Machine Learning Engineer
- Data Scientist
- Generative AI Developer
- Computer Vision Engineer
- Cloud AI Engineer
- Responsible AI Specialist
Leadership Roles
- AI Product Manager
- Head of AI / Director of Data Science
- AI Transformation Officer
- Chief AI Officer (CAIO)
- AI Governance & Risk Director
Career Growth Ladder
| Level | Typical Roles | Relevant Certifications |
|---|---|---|
| Entry-Level | AI Technician, Data Analyst | CAIP |
| Mid-Level | AI Engineer, ML Engineer, AI Developer | CAIE, CRAIS, CGAIS |
| Advanced | AI Architect, AI Consultant, Security Specialist | CLLMS, CCVE, CAICE, CAISPS, Industry Certs |
| Leadership | Head of AI, AI Product Manager, CAIO | CAIL, CAITO, CAIGR, CEAIR |
Certification Pathway
GlofAI Learning Path (Certification Progression)
Stage 1 — Foundation
Stage 2 — Core AI Competency
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
Stage 1 — Foundation
Goal: Build baseline AI knowledge and confidence.
Certified AI Practitioner (CAIP) – Foundational skills in AI, data, and ML.
Stage 2 — Core AI Competency
Goal: Master model development and deployment.
- Certified AI Engineer (CAIE) – Core technical certification.
- Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
- Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
Stage 3 — Specialized Technical Expertise
Goal: Deep dive into specific domains of AI technology.
- Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
- Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
- Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
- Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
Stage 4 — Industry-Specific Applications
Goal: Apply AI to real-world sectors.
- Finance & Banking: Certified AI in Financial Services (CAIFS)
- Healthcare: Certified AI in Healthcare (CAIH)
- Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
- Manufacturing: Certified AI in Manufacturing (CAIM)
- Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
Stage 5 — Executive & Enterprise Leadership
Goal: Lead AI strategy and enterprise adoption.
- Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
- Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
- Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
- Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
| Level | Focus | Example Certifications |
|---|---|---|
| Beginner | Foundations | CAIP |
| Intermediate | Core AI Skills | CAIE, CRAIS, CGAIS |
| Advanced | Specialized Technical | CLLMS, CCVE, CAICE, CAISPS |
| Expert | Industry Applications | CAIFS, CAIH, CAIRSC, CAIM, CAIPG |
| Leader | Strategic & Enterprise | CAIL, CAITO, CEAIR, CAIGR |