MLOps Specialist

hace 2 meses

Madrid, Madrid, España Adyen A tiempo completo

About Adyen

Adyen is a leading financial technology platform that offers a comprehensive suite of payment, data, and financial solutions for prominent clients such as Meta, Uber, H&M, and Microsoft. Our focus is on delivering engineered solutions that enhance operational efficiency.

We are looking for an experienced MLOps or DevOps Engineer to contribute to our Generative AI team in Madrid. This team's objective is to develop a Generative AI platform that supports various applications utilizing Large Language Models (LLMs). The focus is on creating platform components for internal deployment and providing end-to-end solutions that improve operational workflows. Through practical applications like support case routing and sentiment analysis, the team demonstrates the transformative potential of AI across diverse organizational domains.

Our goal is to establish a Generative AI platform at Adyen that facilitates multiple applications based on LLMs. This includes developing platform-oriented components for deploying an LLM backend within Adyen's GPU cluster in Kubernetes, incorporating features such as monitoring, access control, rate limiting, prompt debugging, and experiment tracking. We primarily utilize Open Source frameworks like HuggingFace and LangChain, along with models such as Llama or Mixtral. This role encompasses both the development of platform components and the execution of promising use cases across various sectors within the organization.

Key Responsibilities:

  • Collaborate with the team to architect and construct the infrastructure necessary for hosting LLMs in-house, with a focus on scalability, performance, and reliability.
  • Take ownership of the deployment strategy for ML models aimed at downstream tasks such as ticket routing (text classification), summarization, sentiment analysis, and question-answer retrieval.
  • Automate the ML pipeline utilizing MLOps tools and methodologies, optimizing it for both scalability and performance.
  • Containerize applications and oversee Kubernetes deployments, as well as the infrastructure required for internal LLM deployment, including GPUs, vector databases, and inference components.
  • Establish observability best practices for the entire LLM infrastructure and create an internal framework that enables the team to monitor LLM behavior, ensuring robustness in real-world conditions.
  • Design and implement APIs, services, or frameworks that facilitate seamless integration and utilization of LLMs across various applications and services.
  • Stay informed about the latest advancements in MLOps tools and practices.

Qualifications:

  • 5+ years of professional experience in roles such as DevOps Engineer, MLOps Engineer, ML Engineer, or Data Engineer.
  • Strong software development capabilities, including version control (e.g., Git, preferably on GitLab), coding best practices, debugging, and unit and integration testing.
  • Proficient in Python, Airflow, MLflow, Docker, Kubernetes, and ArgoCD.
  • Familiarity with observability tools such as Prometheus, Logsearch, Kibana, and Grafana.
  • Knowledge of data pipelines and ETL processes for preparing and managing data for ML training and inference, as well as frameworks for model development and deployment.
  • Solid understanding of DevOps best practices and tools for automating software development and deployment processes, including CI/CD concepts and experience implementing these practices.
  • Ability to diagnose and resolve issues related to model performance, scalability, and deployment.
  • Experience with monitoring tools to track model performance, resource utilization, and system health, including 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 managing Kubernetes configurations.
  • Familiarity with infrastructure as code using tools like Terraform.
  • Experience with open-source machine learning frameworks such as Huggingface Transformers.
  • General LLMOps experience is a plus, including model deployment, monitoring, resource management, and infrastructure management, particularly with GPUs.

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