Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, HM;, and Microsoft - making us the financial technology platform of choice.
At Adyen, everything we do is engineered for ambition.Adyen seeks a MLOps or DevOps Engineer to join our Generative AI team in Madrid.
The mission of this team is to create a Generative AI platform enabling various applications with Large Language Models (LLMs).
The team focuses on developing platform components for internal deployment and delivering end-to-end solutions for operational efficiency.
Through use cases like support case routing and sentiment analysis, they showcase AI's adaptability across different domains within the organization, revolutionizing workflows and decision-making processes.Key Responsibilities:Collaborate with the team to design and build the infrastructure to host LLMs in-house while thinking about scale, performance, and reliability.Own the deployment strategy of ML models for downstream tasks such as ticket routing (text classification), summarization, sentiment analysis, and question-answer retrieval.Automate the ML pipeline using MLOps tools and practices and optimize it for scalability and performance.Containerize applications and manage the Kubernetes deployments as well as the infrastructure needed to deploy LLMs internally; from GPUs to vector databases and inference components.Develop observability best practices for the whole LLM infrastructure and build the internal framework which allows the team to monitor the LLM behavior to ensure their robustness under real life conditions.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.Qualifications:5+ years of professional experience as a DevOps Engineer, MLOps Engineer, ML Engineer, or Data Engineer.Strong software development skills, including version control (e.g., Git and preferably on Gitlab), coding best practices, debugging, unit and integration testing.Proficient in Python, Airflow, MLflow, Docker, Kubernetes, and ArgoCD.Proficiency with observability tools, such as Prometheus, Logsearch, Kibana, and Grafana.Knowledge of data pipelines and ETL processes to prepare and manage data for ML training and inference, as well as model development and deployment frameworks.Solid understanding of DevOps best practices and tools to automate software development and deployment processes, and CI/CD concepts and experience in implementing these practices.Ability to diagnose and resolve model performance, scalability, and deployment issues.Familiarity with monitoring tools to track model performance, resource utilization, and system health.
Experience in logging and error monitoring for ML models and applications.Knowledge of ETL pipelines using PySpark and Airflow for data preprocessing and model training.
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