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Devops Engineer
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About AI Square
AI Square is a leading provider of innovative AI solutions, empowering businesses to unlock the full potential of artificial intelligence. Our mission is to create a seamless and efficient AI platform, enabling various applications to thrive.
Job Summary
We are seeking a highly skilled AI 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
- Design and Build Infrastructure: Collaborate with the team to design and build the infrastructure to host LLMs in-house, considering scale, performance, and reliability.
- Deploy ML Models: Own the deployment strategy of ML models for downstream tasks such as ticket routing (text classification), summarization, sentiment analysis, and question-answer retrieval.
- Automate ML Pipeline: Automate the ML pipeline using MLOps tools and practices and optimize it for scalability and performance.
- Containerize Applications: 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: 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 APIs and Services: 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: 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.
