SBS - GenAI R&D Automation Testing Senior Software Quality Engineer - SBS - Paris

SBS is a global financial technology company that’s helping banks and the financial services industry to reimagine how to operate in an increasingly digital world. SBS is a trusted partner of more than 1,500 financial institutions and large-scale lenders in 80 countries worldwide, including Santander, Societé Generale, KCB Bank, Kensington Mortgages, Mercedes-Benz, and Toyota FS. Its cloud platform offers clients a composable architecture to digitize operations, ranging from banking, lending, compliance, to payments, and consumer and asset finance. With 3,400 employees in 50 offices, SBS is recognized as a Top 10 European Fintech company by IDC and as a leader in Omdia’s Universe: Digital Banking Platforms. SBS is headquartered in Paris, France.

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:  

1. Vector database performance and accuracy testing 

1. Retrieval quality metrics and relevance scoring 

1. LLM response validation and hallucination detection 

1. End-to-end agent conversation flow testing 

Develop specialized test suites for AI/ML components:  

1. Knowledge base ingestion and chunking strategies 

1. Embedding quality and semantic search accuracy 

1. Prompt injection and security vulnerability testing 

1. Multi-modal content handling (documents, tables, images) 

Create automated evaluation frameworks for:  

1. Agent response accuracy and consistency 

1. Contextual understanding and reasoning capabilities 

1. Performance benchmarking across different LLMs 

1. A/B testing for prompt engineering optimization 

Collaborate with AI engineers to:  

1. Define quality metrics for RAG architectures 

1. Establish ground truth datasets for evaluation 

1. Implement continuous monitoring for model drift 

1. Design test scenarios for edge cases and failure modes 

Build testing infrastructure for:  

1. Multi-tenant agent deployments 

1. Knowledge base versioning and rollback testing 

1. API rate limiting and scalability testing 

1. Integration testing with customer systems 

Ensure compliance and safety:  

1. Test for bias and fairness in AI responses 

1. Validate data privacy and security measures 

1. Implement guardrails testing for harmful content 

1. 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. 

Required Skills: 

Education: Bachelor's 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:  

1. Experience testing LLM applications, chatbots, or conversational AI 

1. Understanding of RAG architectures and vector databases (Pinecone, Weaviate, Qdrant) 

1. Familiarity with embedding models and similarity search concepts 

1. Knowledge of prompt engineering and LLM evaluation metrics 

Technical Skills:  

1. Proficiency in Python for test automation and AI/ML frameworks 

1. Experience with LLM frameworks (LangChain, LlamaIndex, Haystack) 

1. API testing for RESTful services and streaming endpoints 

1. Familiarity with ML testing tools (MLflow, Weights & Biases, Neptune) 

Automation Frameworks:  

1. pytest, unittest for Python-based testing 

1. Experience with async testing for streaming responses 

1. Load testing tools for AI endpoints (Locust, K6) 

1. CI/CD integration with model deployment pipelines 

Domain Knowledge:  

1. Understanding of NLP concepts and evaluation metrics (BLEU, ROUGE, BERTScore) 

1. Knowledge of information retrieval metrics (precision, recall, MRR) 

1. Familiarity with financial services use cases for AI agents 

1. Understanding of responsible AI principles 

Preferred Qualifications: 

1. Experience with cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI) 

1. Knowledge of vector database optimization and indexing strategies 

1. Familiarity with fine-tuning and model evaluation workflows 

1. Experience with multilingual AI systems testing 

1. Understanding of regulatory requirements for AI in financial services (EU AI Act, GDPR) 

1. Contributions to open-source AI/ML testing frameworks 

Les avantages à nous rejoindre :

• Un accord télétravail pour télétravailler jusqu'à 2 jours par semaine selon vos missions.
• Un package avantages intéressants : une mutuelle, un CSE, des titres restaurants, un accord d'intéressement, des primes vacances.