ALM - Gen AI Quality Engineering
Required Skills & Qualifications

8-12 years of experience in Quality Engineering
3+ years of hands-on experience in testing GenAI applications/
Strong expertise in API testing, automation frameworks and release governance
Deep understanding of:
RAG
Prompt engineering
LLM evaluation techniques (Evals)
Agent workflows
Strong knowledge of API security testing and OAuth-based systems
Experience integrating testing within CI/CD pipelines
Cloud platform familiarity (AWS)
Excellent communication and stakeholder management skills

Role Summary

We are seeking a Senior Lead Quality Engineer (Gen AI) to define and drive the end-to-end Quality Engineering strategy for AI-powered systems, including LLM-based applications, AI assistants, Retrieval-Augmented Generation (RAG) pipelines, and agent-driven workflows.

This role will establish measurable AI quality standards, governance frameworks, automation strategy, and release readiness criteria. The ideal candidate brings strong QE leadership along with hands-on expertise in LLM behavior, contextualization techniques (Prompt Engineering, RAG, Fine-Tuning), evaluation frameworks (Evals), accessibility, API security, and AI observability.

You will work cross-functionally with Product, Engineering, and Platform teams to ensure AI systems are accurate, safe, reliable, scalable, and production-ready.

Key Responsibilities
AI Quality Strategy

Define and own the AI Quality Engineering roadmap and standards
Establish measurable success criteria for Gen AI features
Design quality gates and release readiness frameworks
Lead AI risk assessment and regression strategy

AI Feature & Evaluation Understanding

Define and track AI-specific KPIs, including:

Intent accuracy
Context retention (multi-turn reliability)
Hallucination rate
Bias & toxicity scores
Guardrail breach frequency
Fallback & recovery rate
Retrieval precision/recall (RAG)
Token efficiency
P95/P99 latency

Additional responsibilities:

Design benchmark datasets for deterministic AI regression
Implement automated and human-in-the-loop evaluation frameworks
Build structured AI scoring rubrics and quality dashboards

Contextualization & LLM Validation

Design and validate prompt engineering strategies
Validate RAG (chunking, embeddings, retrieval relevance)
Evaluate fine-tuned models and dataset integrity
Validate agent-based and multi-step reasoning workflows

Safety, Security & Responsible AI

Ensure PII protection and data boundary enforcement
Conduct API security validation, including:
Authentication & OAuth flows
Role-based access control
Cross-account/user isolation
Rate limiting and throttling
Malformed input and abuse scenarios
Ensure compliance with enterprise AI governance standards

Accessibility

Validate compliance with WCAG standards
Ensure screen reader and keyboard navigation compatibility

Performance, Reliability & Observability

Define and validate latency benchmarks and SLAs
Conduct scalability and concurrency testing
Monitor token usage and cost efficiency
Validate feedback, fallback mechanisms and recovery behavior

Automation & CI/CD Integration

Build AI-focused automated regression frameworks
Integrate AI validation into CI/CD pipelines
Enable continuous AI quality monitoring across releases
Ensure backward compatibility across API and platform versions

About Adobe

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