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.