Client: Our client is one of the largest airlines in South America.
- Position overview: We are looking for our next Machine Learning Engineer for the Advanced Analytics team for one of our clients. This professional will be responsible for promoting the execution of the company through our Data Products. They connect models with the real world and are in charge of both making them available in a highly scalable environment and connecting their interactions with reality.
- Responsibilities: Work directly with the Data Scientists team to put Machine Learning models into production by creating and using ML pipelines
- Collection of large volumes and varied data sets
- Collection of interaction with reality for subsequent retraining
- Build the necessary components to serve our models and enable them to interact with the rest of the company in a real and highly scalable environment
- Work very closely with Data Scientists looking for efficient ways to monitor, operate, and give explainability to the models
- Promote a technical culture by boosting the MLOps level of our data products
- Requirements: At least 2 years of experience in work environments as a Software Engineer, Data Engineer, or ML Engineer
- Demonstrated experience in the creation and utilization of generative AI using
- Solid experience with Python, UNIX environments, using CI/CD and Docker pipelines
- Experience with Large Language Models (LLM) - Mandatory
- Experience with Prompt Engine Patterns, Vector DB, or Sabre Integration - Nice to have at least one of them.
- Excellent analytical skills related to working with unstructured data sets, and advanced knowledge of SQL, including query optimization
- Understanding of the complete life flow of Machine Learning models
- Advanced oral and written English
- Excellent communication skills and collaborative work experience
- Nice to have: Experience in building and optimizing highly scalable data pipelines, message queues, and big data architectures
- Experience configuring CI/CD pipelines
- Experience with GCP, especially the machine and data processing suite of services learning
- Ability to visualize possible improvements, problems, and solutions for architectures
