Devops Engineer

hace 1 mes

Madrid, Madrid, España Greenhouse - Ats A tiempo completo

Job Summary

Greenhouse - Ats is seeking a highly skilled Cloud Infrastructure Specialist to join our team in Madrid. As a key member of our Generative AI team, you will be responsible for designing and building the infrastructure to host Large Language Models (LLMs) in-house, ensuring scale, performance, and reliability.

Key Responsibilities:

  • Collaborate with the team to design and build the infrastructure to host LLMs in-house, considering scalability, 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, optimizing it for scalability and performance.
  • Containerize applications and manage 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 to monitor LLM behavior, ensuring robustness under real-life conditions.
  • Design and implement APIs, services, or frameworks to facilitate 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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