Certified LLM Systems Engineer (C-LLMSE) Certification Program by Tonex
This program prepares engineers to design, optimize, and operate Large Language Model (LLM) inference systems at scale. You will learn practical methods for throughput, latency, and cost control across GPUs and CPUs. The curriculum covers quantization, pruning, distillation, graph-level fusion, and memory planning. You will compare eval suites and build robust evaluation pipelines that align with product goals.
The program also covers serving architectures, autoscaling, caching, and observability. You will learn to measure, trace, and tune performance under real workloads. Security is first-class. We discuss prompt injection defenses, data leakage controls, model abuse detection, and governance for regulated environments. You will map controls to policies and audit trails. The outcome is confidence to ship reliable, secure, and efficient LLM inference. Graduates can make clear trade-offs, justify designs, and deliver predictable SLOs.
Learning Objectives:
- Optimize LLM inference for latency, throughput, and cost
- Apply quantization, pruning, and distillation safely
- Build evaluation pipelines with meaningful metrics
- Design resilient serving and autoscaling patterns
- Implement tracing, observability, and capacity planning
- Harden inference against abuse and leakage
Audience:
- ML/LLM Engineers
- MLOps/Platform Engineers
- Software Architects
- Site Reliability Engineers
- Data Scientists
- Cybersecurity Professionals
Program Modules:
Module 1: Inference Optimization Fundamentals
- Latency/throughput modeling and bottleneck analysis
- Kernel fusion and operator scheduling
- KV-cache strategies and reuse patterns
- Batch sizing, dynamic batching, and queuing
- Token parallelism vs. tensor/pipeline parallelism
- Cost modeling and SLO trade-offs
Module 2: Quantization and Compression
- Post-training vs. QAT workflows
- AWQ, GPTQ, and activation-aware methods
- 8-bit/4-bit formats, accuracy risks, and guardrails
- Outlier handling and calibration sets
- Pruning, sparsity, and structured vs. unstructured cuts
- Memory planning and bandwidth optimization
Module 3: Distillation and Model Slimming
- Teacher–student setup and loss design
- Layer mapping and intermediate hints
- Data curation and synthetic augmentation
- Safety and bias preservation checks
- Throughput vs. quality trade-off evaluation
- Release gating and rollback criteria
Module 4: Serving Architectures and Scaling
- Single-tenant vs. multi-tenant isolation
- LoRA/PEFT hot-swap strategies
- Sharding, routing, and session affinity
- Caching layers: prompt, KV, and response
- Autoscaling signals and cooldown logic
- Failover, canarying, and capacity buffers
Module 5: Evaluation and Reliability Engineering
- Eval suite design: unit, integration, and task evals
- Golden sets, drift monitors, and guardrails
- Latency/quality dashboards and error budgets
- Trace-based debugging and flame graphs
- Load testing and traffic replay methods
- Incident playbooks and postmortems
Module 6: Security, Compliance, and Governance
- Prompt injection and jailbreak defenses
- PII redaction and data minimization
- Abuse detection, rate limits, and quotas
- Model carding and evaluation documentation
- Audit trails, retention, and policy mapping
- Secure release management and approvals
Exam Domains:
- Inference Throughput and Latency Engineering
- Compression Strategies and Accuracy Assurance
- Distillation Design and Data Strategy
- Serving Topologies and Scale Operations
- Evaluation Methodologies and Reliability SLOs
- Security, Compliance, and Governance Controls
Course Delivery:
The course is delivered through a combination of lectures, interactive discussions, hands-on workshops, and project-based learning, facilitated by experts in the field of Certified LLM Systems Engineer (C-LLMSE). 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 LLM Systems Engineer (C-LLMSE).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified LLM Systems Engineer (C-LLMSE) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to build fast, reliable, and secure LLM inference? Enroll now. Advance your systems skills and deliver measurable impact.