As a GenAI QA Engineer you will ensure the quality and reliability of our RAG-based AI agent platform. Your responsibilities include:
Design and implement automated testing frameworks for RAG pipelines including:
- Vector database performance and accuracy testing
- Retrieval quality metrics and relevance scoring
- LLM response validation and hallucination detection
- End-to-end agent conversation flow testing
Develop specialized test suites for AI/ML components:
- Knowledge base ingestion and chunking strategies
- Embedding quality and semantic search accuracy
- Prompt injection and security vulnerability testing
- Multi-modal content handling (documents tables images)
Create automated evaluation frameworks for:
- Agent response accuracy and consistency
- Contextual understanding and reasoning capabilities
- Performance benchmarking across different LLMs
- A/B testing for prompt engineering optimization
Collaborate with AI engineers to:
- Define quality metrics for RAG architectures
- Establish ground truth datasets for evaluation
- Implement continuous monitoring for model drift
- Design test scenarios for edge cases and failure modes
Build testing infrastructure for:
- Multi-tenant agent deployments
- Knowledge base versioning and rollback testing
- API rate limiting and scalability testing
- Integration testing with customer systems
Ensure compliance and safety:
- Test for bias and fairness in AI responses
- Validate data privacy and security measures
- Implement guardrails testing for harmful content
- Document AI system limitations and failure modes
Develop comprehensive test strategies for RAG-based AI agents.
Create automated benchmarks for retrieval quality and response accuracy.
Design adversarial testing scenarios to identify system vulnerabilities.
Build dashboards for monitoring AI system performance in production.
Collaborate with customers to understand their AI agent requirements.
Contribute to AI safety and alignment best practices.
Qualifications :
Required Skills:
Education: Bachelors degree in Computer Science Engineering AI/ML or related field.
Experience: 5 years in software testing with at least 2 years focused on AI/ML systems.
AI/ML Testing Expertise:
- Experience testing LLM applications chatbots or conversational AI
- Understanding of RAG architectures and vector databases (Pinecone Weaviate Qdrant)
- Familiarity with embedding models and similarity search concepts
- Knowledge of prompt engineering and LLM evaluation metrics
Technical Skills:
- Proficiency in Python for test automation and AI/ML frameworks
- Experience with LLM frameworks (LangChain LlamaIndex Haystack)
- API testing for RESTful services and streaming endpoints
- Familiarity with ML testing tools (MLflow Weights & Biases Neptune)
Automation Frameworks:
- pytest unittest for Python-based testing
- Experience with async testing for streaming responses
- Load testing tools for AI endpoints (Locust K6)
- CI/CD integration with model deployment pipelines
Domain Knowledge:
- Understanding of NLP concepts and evaluation metrics (BLEU ROUGE BERTScore)
- Knowledge of information retrieval metrics (precision recall MRR)
- Familiarity with financial services use cases for AI agents
- Understanding of responsible AI principles
Preferred Qualifications:
- Experience with cloud AI services (AWS Bedrock Azure OpenAI Google Vertex AI)
- Knowledge of vector database optimization and indexing strategies
- Familiarity with fine-tuning and model evaluation workflows
- Experience with multilingual AI systems testing
- Understanding of regulatory requirements for AI in financial services (EU AI Act GDPR)
- Contributions to open-source AI/ML testing frameworks
Informations supplmentaires :
Les avantages nous rejoindre :
- Un accord tltravail pour tltravailler jusqu 2 jours par semaine selon vos missions.
- Un package avantages intressants : une mutuelle un CSE des titres restaurants un accord dintressement des primes vacances.
Remote Work :
Yes
Employment Type :
Full-time