Certified GenAI Systems Engineer (CGAISE) Certification Program by Tonex
The Certified GenAI Systems Engineer program equips practitioners to design, deploy, and govern production-grade generative AI. Participants learn how foundation models work, how to adapt and evaluate them, and how to operate LLMs reliably with observability, quality monitoring, and responsible release processes. You will master RAG architectures, prompt and data pipelines, and multi-modal integrations aligned to enterprise SLAs and compliance.
Cybersecurity is a first-class concern throughout this program. You will learn how to reduce prompt injection risk, contain data leakage, and harden interfaces and endpoints. Practical governance frameworks translate cybersecurity controls into measurable safeguards for generative AI. Graduates exit ready to ship robust, safe, and cost-efficient GenAI services in real organizations.
Learning Objectives
- Explain foundation model architectures and tokenization fundamentals
- Implement guardrails for safety, privacy, and policy enforcement
- Design LLMOps pipelines for fine-tuning, evaluation, and rollout
- Build reliable RAG systems with retrieval quality metrics
- Integrate multi-modal inputs and outputs with latency budgets
- Apply cybersecurity controls to defend against model abuse and data exfiltration
- Establish KPIs, SLAs, and monitoring for ongoing model operations
Audience
- AI and ML Engineers
- Data Scientists and MLOps Engineers
- Software and Platform Engineers
- Solution Architects and Product Managers
- Cybersecurity Professionals
- Compliance, Risk, and Governance Leads
Course Modules
Module 1: Foundation Models
- Transformer anatomy and scaling laws
- Tokenization, embeddings, and context windows
- Pretraining data, objectives, and bias
- Inference paths and latency drivers
- Evaluation beyond accuracy
- Cost and capacity planning
Module 2: Safety Guardrails
- Content policy design and taxonomy
- Prompt hardening and input validation
- Output filtering and red-team strategies
- Safety scorecards and thresholds
- Abuse monitoring and triage flows
- Regulatory alignment and documentation
Module 3: LLMOps Lifecycle
- Data curation and governance
- Fine-tuning and preference optimization
- Canary, shadow, and phased rollout
- Offline and online evaluation loops
- Observability and incident playbooks
- Cost controls and autoscaling patterns
Module 4: RAG Systems
- Retrieval patterns and index selection
- Chunking, grounding, and citation
- Query rewriting and routing
- Freshness, feedback, and re-ranking
- Metrics for attribution and hallucination
- Security for connectors and stores
Module 5: Multi-Modal AI
- Vision and audio encoders overview
- Text-image alignment techniques
- Structured tool use and function calling
- Streaming and real-time interaction
- UX patterns for explainability
- Edge and on-prem considerations
Module 6: Governance & Risk
- Model risk assessment process
- Policy-as-code and approvals
- Data residency and lineage tracking
- Third-party model due diligence
- Cybersecurity threat modeling for GenAI
- Audit trails and reporting artifacts
Exam Domains
- GenAI Fundamentals and Architectures
- Safety Engineering and Risk Controls
- LLMOps and Model Lifecycle
- Retrieval-Augmented Generation Design
- Multi-Modal Integration and Tools
- Governance, Compliance, and Auditing
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, and project-based learning, facilitated by experts in the field of Certified GenAI Systems Engineer (CGAISE). 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 GenAI Systems Engineer (CGAISE).
Question Types
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria
To pass the Certified GenAI Systems Engineer (CGAISE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to lead safe, reliable GenAI in production Join the CGAISE Certification Program by Tonex and transform your organization’s AI capabilities. Enroll now.