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DevOps Engineer
hace 1 mes
Adyen's Generative AI Platform
Adyen is seeking a skilled 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.
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, Data Engineer.
- Strong software development skills, including version control (e.g., Git and preferable 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.
Desirable Additional Requirements:
- 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.
- General LLMOps experience is a plus, including model deployment, monitoring, resources, and infrastructure management, including GPU knowledge.