Certified AI Engineering Professional (C-AIEP) Certification Program by Tonex
The Certified AI Engineering Professional program develops end-to-end AI builders. It covers data acquisition, feature pipelines, model design, training, and deployment. You learn to ship reliable AI services that integrate with APIs, microservices, and enterprise systems. The curriculum spans ML, DL, and Generative AI with a delivery mindset.
We emphasize scalability, observability, and cost control. You practice versioning, testing, and CI/CD for models and data. Security is embedded throughout. You learn threat modeling for AI pipelines, model hardening, and secure integration patterns. We address privacy, governance, and compliance for regulated environments. The outcome is practical skill and architectural judgment. You will be ready to move from notebook to production and support real business outcomes.
Learning Objectives:
- Build full-stack AI systems from data to API.
- Design and train ML, DL, and GenAI models.
- Implement feature stores and reproducible pipelines.
- Deploy models with CI/CD and automated testing.
- Integrate AI with REST/gRPC and event streams.
- Monitor drift, quality, and cost in production.
- Apply security, privacy, and governance controls.
- Deliver business value with clear KPIs.
Audience:
- AI/ML Engineers
- Data Engineers
- Software Architects and Developers
- DevOps and MLOps Engineers
- Product Managers and Technical Leads
- SRE and QA Professionals
- Cybersecurity Professionals
- Business Analysts and Project Managers
Program Modules:
Module 1: Foundations of Full-Stack AI Engineering
- AI system lifecycle and roles
- Problem framing and KPIs
- Data contracts and SLAs
- Reproducibility and versioning
- Experiment tracking basics
- Cost, latency, and reliability trade-offs
Module 2: Data Pipelines and Feature Engineering
- Batch and streaming ingestion
- Data quality checks and profiling
- Feature stores and lineage
- Handling imbalance and bias
- Synthetic data and augmentation
- Secure data access patterns
Module 3: Model Design, Training, and Evaluation
- Classical ML and DL choices
- Loss functions and metrics
- Hyperparameter search strategies
- GenAI fine-tuning and RAG
- Robust evaluation and A/B design
- Fairness and robustness testing
Module 4: APIs, Microservices, and Integration
- Service patterns for inference
- REST, gRPC, and async queues
- Caching and rate limiting
- Schema and contract testing
- Observability and tracing
- Enterprise integration strategies
Module 5: MLOps, Deployment, and Monitoring
- CI/CD for data and models
- Containers and orchestration
- Blue-green and canary releases
- Drift, performance, and cost monitors
- Automated rollback policies
- Incident response runbooks
Module 6: Governance, Security, and Reliability
- Model governance and approvals
- Threat modeling for AI pipelines
- PII protection and privacy by design
- Adversarial risks and model hardening
- Compliance and audit readiness
- Business continuity and resilience
Exam Domains:
- AI Engineering Principles and Patterns
- Secure Data Lifecycle Management
- Model Development and Optimization
- MLOps, CI/CD, and Observability
- AI Governance, Risk, and Compliance
- AI Solutions Architecture and Integration
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in Certified AI Engineering Professional (C-AIEP). Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Engineering Professional (C-AIEP).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Engineering Professional (C-AIEP) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build production-grade AI systems? Enroll now and accelerate your path from prototype to platform. Contact Tonex to schedule your cohort or request a custom program.