Machine Learning Engineer

Client

Our client is the leading airline in Latin America, offering the broadest network of destinations, flight frequencies, and aircraft fleet in the region. The company is driving innovation and quality through advanced

AI/ML technologies.

Project overview

You will join a strategic initiative focused on enhancing operational safety and predictive capabilities using advanced AI and machine learning. The project is in the early discovery phase, with a strong emphasis on GenAI and LLM-based solutions. Your role will involve leading the development and deployment of these models, integrating them into scalable, production-ready systems.

Position overview

This position focuses on leading the development and deployment of GenAI and LLM-based solutions to enhance operational safety and predictive capabilities, integrating them into scalable, production-ready systems.

Responsibilities

Lead the design and development of GenAI and LLM-based solutions to address real-world challenges in aviation

• Ensure scalable and reliable deployment of ML and LLM models in production environments
• Collaborate with DevOps and SRE teams to define and improve practices tailored to GenAI/LLM solutions
• Build internal tools to streamline model development, evaluation, and deployment processes
• Operate and monitor AI/ML platforms and systems, ensuring performance, availability, and rapid response to incidents
• Contribute to the continuous optimization of CI/CD pipelines and infrastructure

Requirements

• Strong experience with Google Cloud Platform (GCP)
• Proficiency in Python and key ML libraries
• Experience deploying and maintaining ML systems in production
• Familiarity with infrastructure as code tools such as Terraform
• Hands-on experience with containerization (Docker) and CI/CD pipelines
• Solid understanding of ML lifecycle, from data preparation to model monitoring
• Experience with ML Ops tools (e.g., Airflow, MLflow)
• Background in developing and scaling LLMs or GenAI solutions
• Understanding of observability and incident response in AI/ML production environments