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GenAI Safety & Evaluation Engineering

L5-L6 · 306h · 7 courses · 102 chapters

Design automated LLM evaluation pipelines, red-team GenAI systems, build bias detection and fairness benchmarks, implement guardrails.

Role-alignedHands-on labsCapstone project30-day money-back

What you'll own in this role

Core responsibilities this discipline prepares you for.

Build automated evaluation pipelines

to continuously measure LLM output quality

Design evaluation harnesses with RAGAS, DeepEval, and NeMo Evaluator SDK for multi-metric scoring
Create evaluation datasets with ground-truth annotations and run cross-provider comparisons
Wire CI gates that automatically block deployments when faithfulness or relevance scores degrade

Conduct red-team exercises

— probe LLMs for vulnerabilities

Automate adversarial testing with Garak for prompt injection, jailbreak, and data extraction probes
Run multi-turn adversarial campaigns with Meta GOAT and DeepTeam for agent vulnerability testing
Execute red-team campaigns against realistic systems, discover vulnerabilities, and write actionable findings

Implement production guardrails

— content filters, PII detection, jailbreak prevention

Configure NeMo Guardrails with Colang policy language, Llama Guard 4, and Prompt Guard 2
Add Presidio for PII detection/redaction and Model Armor for Google-native content safety
Layer multiple defenses, test against comprehensive attack suites, and quantify safety-vs-helpfulness tradeoffs

Design GenAI governance frameworks

aligned with regulations

Map EU AI Act risk classification and implement NIST AI RMF control frameworks
Build OWASP LLM Top 10 mitigation strategies mapped to technical controls
Create governance artifacts, conduct risk assessments, and build automated audit trail pipelines

Evaluate GenAI agent behavior

— trajectory quality, tool selection accuracy

Build trajectory scoring systems measuring tool selection accuracy and task completion quality
Design human preference alignment tests and regression test suites for agent workflows
Evaluate multi-step agent executions to identify failure modes and build targeted regression tests

Monitor bias, fairness, and hallucination rates

in production

Detect bias across protected attributes using statistical fairness metrics and disparity analysis
Measure hallucination rates through ground-truth comparison and citation verification
Implement continuous bias scanning, hallucination detection, and alerting for metric drift

Build safety incident response processes

for deployed GenAI systems

Design safety monitoring dashboards with severity-based alert routing and escalation paths
Build incident triage workflows with containment procedures and post-incident reporting templates
Simulate safety incidents end-to-end and practice the full detection-to-resolution workflow

Design LlamaFirewall policies

for agent safety

Configure LlamaFirewall middleware for controlling agent tool access and output filtering rules
Set up multi-agent safety boundaries with policy-based execution constraints
Validate firewall policies against adversarial scenarios where agents attempt to bypass controls

Tools you'll ship with

Industry-standard stack for current L4–L6 GenAI engineering roles.

DeepEvalRagasArize PhoenixGuardrails AINeMo GuardrailsLlama GuardPresidioArgillaOpenAI APIAnthropic APILangfuseK8s

Your learning route

7 courses · sequenced for compounding · 102 chapters · ~306 hours

Step 1 · Foundations

Python Essentials for Agent Builders

13 chapters

Step 2

LLM Foundations for Agent Builders

20 chapters

Step 3

Kubernetes Essentials for GenAI Engineers

17 chapters

Step 4

Web APIs & Services for GenAI Engineers

12 chapters

Step 5

GenAI Agent Engineering

16 chapters

Step 6

GenAI Evaluation, Safety & Governance

14 chapters

Step 7 · Capstone

GenAI Operations

10 chapters

Start the GenAI Safety & Evaluation Engineering discipline today

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