Senior Machine Learning Engineer II

SumUp
Barcelona, Spain
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Barcelona, Spain

Tech stack

Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Azure
Continuous Integration
Python
Machine Learning
Product Management
Data Processing
Feature Engineering
Chatbots
Backend
GIT
Kubernetes
Machine Learning Operations
Software Version Control
Data Pipelines
Api Management
Docker

Job description

Our Global Operations team is on a mission to build the best customer experience in fintech, and AI is at the heart of how we get there. As part of our dedicated AI team, you'll sit at the intersection of machine learning engineering and real-world product impact, building the models and systems that power intelligent customer support for over 4 million merchants worldwide. This isn't a research role; it's a hands-on engineering position where the work you ship directly shapes how merchants experience SumUp every day., * Architect, build, and deploy AI solutions into production, ensuring they're reliable, performant, and built to scale

  • Design and maintain ML infrastructure and pipelines that support efficient data processing, model training, and serving
  • Fine-tune and optimise machine learning models for accuracy, efficiency, and scalability in large-scale environments
  • Collaborate closely with data scientists, product managers, and backend engineers to translate business needs into working AI solutions
  • Apply strong engineering standards across your work, including version control, CI/CD, and testing frameworks
  • Collect, preprocess, and prepare large text datasets to support high-quality model training and evaluation

Requirements

  • Experience building and deploying scalable machine learning or AI solutions in large-scale production environments, with a focus on reliability and performance
  • Strong knowledge of AI product development, including chatbot assistants, RAG systems, or similar conversational AI applications
  • Advanced proficiency in Python, with hands-on experience using MLOps tooling such as MLflow, Kubeflow, Airflow, or Langfuse
  • Comfort working across the full ML workflow, from feature engineering and data pipelines through to model serving, monitoring, and alerting
  • Experience deploying ML models using cloud platforms such as AWS, GCP, or Azure, alongside familiarity with Docker, Git, and API integrations

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