Machine Learning Engineer

hace 4 meses

Madrid, España Intelygenz A tiempo completo

At Intelygenz, we envision a future where processes accelerate performance. For the companies we work with, this means finding ways to optimize their data using cutting-edge technologies like AI-enabled automation. This mindset is a huge part of our culture. We thrive on pooling our skills, collaborating on projects, helping one another learn and creating innovations.What are you going to do?As a Machine Learning Engineer, your responsibilities will be:Designing and implementing end-to-end inference pipelines.Designing and implementing ETL pipelines for AI solutions deployed on production environments.Designing and implementing Feedback Loop for AI solutions deployed on production environments.Fine-tuning of ML and DL models for production purposes.What will make you succeed in your role?Wide experience working with GIT.Minimum experience in deploying and maintaining ML models in production.Minimum experience in working with Docker and/or Kubernetes.Minimum experience working with different database types such as SQL, Document-oriented, Key-Value, and Time Series.Minimum experience working with some event brokers such as Kafka, RabbitMQ, NATS...Experience working with API frameworks.Minimum experience working with Protobuf.Data Architectures designed for exploiting and using Machine Learning techniques.Modeling fine-tune for ML and DL algorithms.Minimum experience using Python 3.8+ and PyTorch.Some experience with GoLang.High control of the different steps regarding the ML experiments to execute.Fluent in English for day-to-day, customer meetings and reporting.Strong self-taught and proactivity capabilities.Problem-solving and autonomy when facing new challenges.Bonus Points:Experience working with Kubernetes.Experience working with Graph databases.Comfortable with TDD/ATDD.Comfortable with fully automated CI/CD environments.Other programming languages: Node.Js, C/C++ or Java.Desire to learn & constant curiosity.Ability to adapt to different environments and projects.#J-18808-Ljbffr

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