Quantexa MLOps Engineer

hace 2 semanas

Málaga, Málaga, España Quantexa A tiempo completo
About Quantexa

Quantexa is a company that enables better decision-making through better data-driven intelligence. Our team is passionate about harnessing complexities and helping our clients and their communities.

Role Overview

As a highly skilled MLOps Engineer, you will be responsible for developing and maintaining the infrastructure and automation pipelines that support our machine learning models. You will work closely with data scientists, data engineers, and other MLOps engineers to deliver robust, scalable machine learning pipelines that are optimized for production environments.

Key Responsibilities
  • Collaborate with data scientists to ensure smooth deployment of machine learning models into production environments.
  • Automate the deployment of machine learning models using CI/CD pipelines and container orchestration tools like Kubernetes and Docker.
  • Ensure proper model versioning, governance, and compliance using tools like MLFlow, Kubeflow, or DVC.
  • Build and maintain data and model pipelines for training, validation, deployment, and monitoring.
  • Develop automated processes for data validation, feature engineering, and model training.
  • Integrate pipelines with distributed data processing frameworks (e.g., Spark, Kafka) to ensure efficient data handling for model training and inference.
  • Set up monitoring systems to track model performance and detect issues like model drift, triggering retraining when necessary.
  • Work with cloud infrastructure to scale models and ensure high availability in production environments.
  • Contribute to the troubleshooting and resolution of issues with models in production.
  • Work closely with data engineers and senior MLOps engineers to ensure consistency across teams and smooth integration of machine learning pipelines.
  • Participate in Scrum ceremonies, ensuring timely delivery of new features and improvements.
Requirements
  • MLOps Expertise: Experience deploying machine learning models into production and managing the full model lifecycle.
  • Good knowledge of the machine learning development lifecycle, with experience building a project end-to-end.
  • Good understanding of NLP.
  • Hands-on experience with MLOps tools like MLFlow, Kubeflow, DVC, or equivalent.
  • Experience with containerization technologies, including Docker and Kubernetes.
  • Experience with Data Engineering Automation & Infrastructure: Experience with CI/CD tools such as Jenkins, GitLab CI, or similar for automating model deployment.
  • Proficiency in cloud platforms.
  • Experience with infrastructure as code tools such as Terraform and Helm for managing cloud-based infrastructure.
  • Programming & Frameworks: Proficiency in Python, including experience with machine learning libraries like PyTorch, TensorFlow, or Scikit-learn.
  • Experience developing and working with REST APIs.
  • Good knowledge of Spark and/or Java.
  • Familiarity with distributed data processing frameworks like Spark, Kafka, or similar.
  • Strong knowledge of BDD/TDD, and general testing principles.
We Offer
  • Competitive salary.
  • Company bonus.
  • Free Calm App Subscription.
  • Ongoing personal development.
  • Great Company-wide socials.
  • Plus other local benefits.

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