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Devops Engineer
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At Greenhouse - Ats, we're seeking a highly skilled Devops Engineer to join our team in Madrid. As a key member of our Generative AI team, you will play a crucial role in 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.
- 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.
- 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.
- 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.
