DevOps Engineer
hace 2 semanas
Adyen is a leading financial technology platform that provides payments, data, and financial products to customers like Meta, Uber, H&M, and Microsoft. Our mission is to create a seamless and efficient payment experience for our customers.
Job DescriptionWe are seeking a highly skilled DevOps Engineer to join our Generative AI team in Madrid. The successful candidate will be responsible for designing and building the infrastructure to host Large Language Models (LLMs) in-house, ensuring scale, performance, and reliability.
The ideal candidate will have a strong background in software development, including version control, coding best practices, debugging, unit and integration testing. Proficiency in Python, Airflow, MLflow, Docker, Kubernetes, and ArgoCD is required. Experience with observability tools, such as Prometheus, Logsearch, Kibana, and Grafana, is also essential.
The successful candidate will be responsible for automating the ML pipeline using MLOps tools and practices, optimizing it for scalability and performance. They will also be responsible for containerizing applications, managing Kubernetes deployments, and designing and implementing APIs, services, or frameworks to facilitate the seamless integration and usage of LLMs within various applications and services.
We are looking for a candidate who is passionate about MLOps and DevOps, with a strong understanding of DevOps best practices and tools to automate software development and deployment processes. The ideal candidate will have a solid understanding of the end-to-end machine learning lifecycle and experience with Open-source Machine Learning frameworks like Huggingface Transformers.
Key Responsibilities- Design and build the infrastructure to host LLMs in-house, ensuring scale, performance, and reliability.
- Automate the ML pipeline using MLOps tools and practices, optimizing it for scalability and performance.
- Containerize applications, manage Kubernetes deployments, and design and implement APIs, services, or frameworks to facilitate the seamless integration and usage of LLMs within various applications and services.
- Stay up to date with the latest advancements in MLOps tools and practices.
- 5+ years of professional experience as a DevOps Engineer, MLOps Engineer, ML Engineer, Data Engineer.
- Strong software development skills, including version control, coding best practices, debugging, unit and integration testing.
- Proficiency in Python, Airflow, MLflow, Docker, Kubernetes, and ArgoCD.
- Experience with observability tools, such as Prometheus, Logsearch, Kibana, and Grafana.
- Solid understanding of DevOps best practices and tools to automate software development and deployment processes.
- Knowledge of ETL pipelines using PySpark and Airflow for data preprocessing and model training.
- Clear understanding of the end-to-end machine learning lifecycle.
- Experience with Helm for packaging and deploying applications on Kubernetes and with Kustomize for customizing and managing Kubernetes configurations.
- Familiarity with infrastructure as code with tools like Terraform.
- Experienced with Open-source Machine Learning frameworks like Huggingface Transformers.
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