AI QA Engineer
Se pueden requerir diversas habilidades interpersonales y experiencia para el siguiente puesto. Por favor, asegúrese de consultar la descripción a continuación con atención.
An exciting opportunity has opened with an innovative AI-focused technology team working on next-generation AI and machine learning solutions at enterprise scale. We’re looking for an experienced AI QA Engineer to help drive quality across cutting-edge AI products, including LLM-powered applications, multi-agent systems, RAG pipelines, and cloud-native AI platforms. This is a hands‑on role suited to someone passionate about AI quality assurance, automation, and testing modern AI architectures in a fast‑moving Agile environment.
What You’ll Be Doing
Designing and maintaining automated test frameworks for AI/ML applications and LLM-powered systems
Testing RAG pipelines, semantic search functionality, and vector database implementations
Building automated testing for data and ETL pipelines
Validating AI APIs, backend services, and cloud-native AI workloads
Developing evaluation frameworks for LLM outputs and non‑deterministic AI behaviours
Working closely with AI Engineers, Data Engineers, and Product teams to define quality standards
Supporting performance, integration, regression, and functional testing across AI systems
Contributing to QA best practices within a collaborative Scrum environment
Tech Environment
Python
Playwright
Cucumber
FastAPI
LangChain / LangGraph
CrewAI
AWS (Bedrock, SageMaker, Lambda, S3, Aurora)
Kubernetes & Docker
Vector Databases (OpenSearch, Pinecone, pgvector)
RAG Evaluation Tools (RAGAS, Hugging Face, LangSmith)
GitHub Actions / CI/CD
What We’re Looking For
3+ years in QA / Test Engineering
Commercial experience testing AI/ML or LLM-based applications
Strong automation testing experience with Python
Experience with Playwright and modern testing frameworks
Understanding xhfqzwm of prompt engineering and LLM evaluation methodologies
Experience testing APIs and cloud-native platforms
Exposure to Kubernetes, Docker, and AWS services
Strong communication skills and a collaborative mindset
Nice to Have
Experience with multi‑agent AI systems
MLOps exposure (MLflow, W&B;, CI/CD for ML)
Observability tooling such as Langfuse or Datadog
Performance testing experience (k6, Locust, JMeter)
Terraform or Infrastructure-as-Code exposure
Location
Barcelona, Spain
Hybrid working model (2–3 days onsite)
#J-18808-Ljbffr