GenAI Safety & Evaluation Engineering
Design automated LLM evaluation pipelines, red-team GenAI systems, build bias detection and fairness benchmarks, implement guardrails.
What a GenAI safety and evaluation engineer does · GenAI Safety & Evaluation Engineering
Before a model output reaches a user, someone has to be able to say how good it is, how often it is wrong, and for whom. This discipline builds that measurement: automated evaluation pipelines with DeepEval and Ragas, red-team exercises that probe for jailbreaks and harmful completions, bias and fairness benchmarks, and guardrails — Guardrails AI, NeMo Guardrails, Llama Guard, Presidio — with policies you can audit. Agent behavior gets the same treatment at the trajectory level. The output is governance a company can defend: incident response for safety failures, LlamaFirewall policies, and dashboards that show hallucination rates moving. Security engineering defends against the attacker; you define what acceptable behavior is and prove the system stays inside it.
Start with these 3 free GenAI Safety & Evaluation Engineering lessons
The same content subscribers read — no account required. Browse all 28 free GenAI Safety & Evaluation Engineering lessons
7 courses, each building on the last. The bar shows each course's share of the curriculum. 28 lessons are free to read.
Python Essentials for Agent Builders 9.4% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 1–13 — 62 of this path’s 657 lessons. 1 free GenAI Safety & Evaluation Engineering lesson
LLM Foundations for Agent Builders 7.6% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 1–6, 14–15, 19–20 — 50 of this path’s 657 lessons. 1 free GenAI Safety & Evaluation Engineering lesson
Kubernetes Essentials for GenAI Engineers 7.3% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 1–4, 9–12 — 48 of this path’s 657 lessons. 1 free GenAI Safety & Evaluation Engineering lesson
Web APIs & Services for GenAI Engineers 9.1% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 1–10 — 60 of this path’s 657 lessons. 1 free GenAI Safety & Evaluation Engineering lesson
GenAI Agent Engineering 35.5% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 11–47, 75, 81 — 233 of this path’s 657 lessons.
GenAI Evaluation, Safety & Governance 22.8% of the curriculum
- Evaluation Dataset CurationBuild a stratified evaluation datasetDetect dataset contamination and leakage
- LLM-as-Judge Evaluation
- RAG Evaluation with RAGAS & DeepEvalImplement RAGAS metrics for RAG evaluationBuild DeepEval test suites for RAG
- Evaluation Observability with Langfuse v3 & OpenTelemetry
- Agent Trajectory EvaluationScore agent tool selection with DeepEval 3.0 and Vertex AI Agent EvaluationBuild agent benchmarks with task suites
- Human-in-the-Loop Evaluation
- A/B Testing for LLM SystemsDesign A/B experiments for prompt variants
- Cross-Model EvaluationBuild cost-performance analysis across providers
- Cost Governance & Token BudgetsDetect cost anomalies and spending spikesBuild cost governance dashboard and chargeback
- Content Safety FiltersCompare guardrail frameworks: Guardrails AI vs NeMo Guardrails 0.20 vs NemoGuard NIMs vs Google Model Armor
- PII Detection & RedactionDetect PII with Presidio and Google Sensitive Data ProtectionImplement reversible PII redactionBuild custom PII recognizers for domain data
- Agent Safety, MCP Security & SandboxingValidate agent tool calls against permission policiesSecure MCP servers and implement agent gateway patternsDetect privilege escalation in agent behaviorBuild agent audit trail with GCP SCC Agent Engine Threat DetectionBuild agent safety evaluation framework
- Vector & Embedding SecurityDetect RAG data poisoning attacksImplement document-level access control for RAGBuild adversarial embedding defenseDetect data exfiltration via RAG
- EU AI Act ComplianceTrack EU AI Act enforcement timeline compliance
- 14 of 25 chapters shownand 11 more
GenAI Operations 8.2% of the curriculum
In scope for GenAI Safety & Evaluation Engineering: chapters 17, 22, 46–49 and 3 more — 54 of this path’s 657 lessons.
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
LLM-as-judge rubric design, position-bias correction, calibration against human raters, scoring functions, judge reproducibility, multi-judge ensembling. The core automated-evaluation technique.
Automated eval pipelines: eval-driven CI/CD, quality gates that block deploys, regression suites, champion/challenger A/B, prompt-variant testing — plus eval-pipeline observability, tracing (Langfuse/OpenTelemetry) and cost governance for judge runs.
Golden-set curation, dataset versioning, prompt-variant generation, edge-case mining, stratified sampling — and creating standardized safety benchmarks: jailbreak/refusal taxonomies, harm-category coverage, threshold-gated safety suites.
Adversarial robustness evaluation: systematic red-team test-suite generation, jailbreak/injection probe batteries, automated red teaming, robustness scoring and attack-success-rate metrics, OWASP LLM Top 10 + MITRE ATLAS. The measured-verdict eval slice, not offensive exploitation.
Statistical fairness evaluation: demographic-parity and equalized-odds tests, disparate-impact ratios, subgroup and intersectional slicing, counterfactual-token swaps (BBQ/CrowS-Pairs/WinoBias), and fairness-benchmark construction. Distinct from factual grounding.
EU AI Act / NIST AI RMF / SOC2 / HIPAA / GDPR control mapping, governance-artifact generation (model cards, eval policies, audit trails, sign-off gates), AI risk classification and governance workflows. Implemented on top of the eval capabilities.
Factual-grounding evaluation: hallucination detectors, faithfulness/groundedness scoring, NLI-based entailment, attribution and citation precision against source context. Distinct from statistical fairness.
Step-level agent evaluation: trajectory scoring, tool-call accuracy, plan-quality assessment, human-in-the-loop review gates, golden-trajectory datasets.
Runtime safety enforcement: content moderation, guardrails, PII detection + redaction, toxicity classifiers, output sanitization, regulated-content classification. The inference-time defense layer (vs. building the eval suite in G3).
RAGAS + DeepEval + TruLens pipelines, retrieval relevance + faithfulness + answer-relevancy metrics, cross-model comparison harnesses and arena/pairwise model ranking.
Provider SDK integration in eval and safety code: judge models, multi-provider scoring, cross-model evaluation harnesses, multi-provider abstraction. Prerequisite plumbing.
Production-grade Python for eval tooling: async/await, Pydantic models for eval rubrics, typing, dataclasses, pytest harnesses, parametrized testing. Prerequisite.