Manual QA EngineerLLM Evaluation Engineer
A transition path from manual / scripted QA—roles increasingly impacted by AI-generated test creation— to a future‑proof specialty focused on designing systematic, metric‑driven evaluation, safety, and reliability processes for Large Language Model (LLM) features (accuracy, faithfulness, relevance, safety, robustness, cost & latency, regression tracking).
Role Overview
Manual QA Engineer
Your current role
- Execute manual and scripted test cases; document defects.
- Validate functional correctness against specifications and acceptance criteria.
- Maintain regression suites and test data sets.
- Perform exploratory testing to find edge cases.
- Communicate issues and reproduction steps to developers and product stakeholders.
LLM Evaluation Engineer
Your target role
- Design and curate evaluation datasets (gold, synthetic, adversarial) for LLM/RAG systems.
- Implement automated metrics: faithfulness, relevance, hallucination rate, context recall, toxicity, bias, latency, cost.
- Build regression & drift monitoring pipelines for prompts, retrieval layers, and model versions.
- Red‑team models (prompt injection, jailbreak, data leakage) and develop mitigations.
- Collaborate with product, ML, security, and compliance to define acceptance thresholds & guardrails.
- Instrument telemetry (token usage, error taxonomy) and drive optimization.
- Maintain documentation of evaluation methodologies, datasets, and change logs.
Why This Transition Works
QA professionals are skilled in systematic test design, edge case discovery, reproducible documentation, and cross‑team communication. These map directly to emerging LLM needs: constructing reliable evaluation datasets, defining meaningful qualitative + quantitative metrics, automating regression suites, and managing model/prompt safety risks—capabilities that remain harder to commoditize than pure prompt crafting.
Transferable Skills
Test Case Design → Prompt & Scenario Suite Design
Transform acceptance criteria into comprehensive prompt/evaluation suites (happy path, edge, adversarial).
Defect Taxonomy → Failure Mode Classification
Extend bug categories to hallucination, bias, toxicity, privacy leak, refusal, tool misuse.
Regression Discipline → Baseline & Drift Tracking
Maintain golden datasets and thresholds; detect metric deltas across model/prompt versions.
Exploratory Testing → Structured Red Teaming
Creative edge case discovery evolves into systematic adversarial & jailbreak test generation.
Clear Bug Reports → Actionable Eval Artifacts
Produce reproducible evaluation records: prompt, context, output, expected rationale, metric diffs.
Cross-Team Communication → Multi-Stakeholder Alignment
Translate qualitative output failures into prioritized engineering or policy improvements.
Foundations
Duration: 4 weeks
Key Activities
- Study LLM basics: tokens, parameters, system vs user messages, context windows.
- Define core evaluation dimensions (faithfulness, relevance, hallucination, safety, robustness, latency, cost).
- Assemble a small golden dataset (≈50 Q&A or task cases) for a familiar domain.
- Implement a minimal evaluation harness (prompt → output → metric calculation) using one framework (e.g. Ragas or promptfoo).
- Start an evaluation journal (date, change, metrics, interpretation).
Core Skill Building
Duration: 6 weeks
Key Activities
- Expand metrics: semantic similarity, faithfulness, hallucination detection, context recall@k, toxicity, bias.
- Introduce retrieval (RAG) scenarios: measure retrieval quality (recall, precision, grounded answer ratio).
- Automate nightly regression runs in CI; persist historical metric trends.
- Create adversarial / red-team set (prompt injection, jailbreak attempts, misleading context).
- Add cost & latency instrumentation (tokens per successful task, p95 latency).
- Compare outputs across multiple models (e.g. GPT vs Claude vs open weights) with the same dataset.
Applied Projects & Specialization
Duration: 6 weeks
Key Activities
- Project: Hallucination reduction—baseline vs improved retrieval/prompt chain, quantify rate drop.
- Project: Retrieval strategy benchmark (chunking overlap or hybrid BM25+vector vs pure vector).
- Project: Adversarial red-team harness with jailbreak taxonomy & mitigation iteration.
- Project: Cost optimization (prompt caching, model tiering) with $/correct answer impact.
- Implement drift detection thresholds (trigger alerts on >X% metric delta).
- Publish case studies (methods, metrics, before/after).
Job Transition & Positioning
Duration: 4 weeks
Key Activities
- Refactor resume: highlight metric improvements (hallucination %, cost/token reduction, recall gains).
- Create portfolio site/dashboard with evaluation trend charts & methodology descriptions.
- Prepare STAR stories (e.g. 'Reduced hallucination rate 22%→7% via retrieval tuning').
- Build boolean search strings targeting 'LLM Evaluation', 'AI Quality', 'AI Safety Engineer'.
- Mock interviews: design an evaluation plan in constrained time.
- Negotiate compensation referencing cost savings & risk mitigation impact.