Client: Our client is a leading airline in Latin America, operating the region's largest network of destinations, flight frequencies, and fleet. The company is driving innovation through advanced AI and Machine Learning initiatives, with a strong focus on next generation Generative AI solutions.
- Project overview: You will join a strategic initiative within the Emantto domain, contributing to the acceleration of the organization's AI portfolio across multiple business areas.
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This role combines ML/AI engineering with strong software engineering, cloud, and infrastructure expertise. You will build and operate Generative AI driven data products, support domain teams, and act as a facilitator for the Data & AI Platform.
Position overview: We are looking for an ML/AI Engineer with solid knowledge of cloud technologies, Infrastructure as Code (IaC), CI/CD practices, and software engineering best practices, focused on designing, building, and operationalizing Generative AI based solutions.
- Responsibilities: Develop and deliver data products and AI/Generative AI solutions within domain teams.
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Act as a facilitator of the Data & AI Platform, enabling adoption and accelerating delivery across teams.
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Build, deploy, and operate Generative AI and Machine Learning models in scalable, production ready environments.
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Manage infrastructure related aspects of environments and AI/ML products, including observability, performance, and reliability.
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Contribute to CI/CD pipelines, Infrastructure as Code practices, and platform automation initiatives.
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Collaborate with cross functional teams, including Software Engineering, Data Engineering, MLOps, and DevOps teams, to maintain high engineering standards.
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Support experimentation frameworks and internal tools for Generative AI model development and evaluation.
- Requirements: Experience working with Google Cloud Platform (GCP).
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Experience with Terraform or other Infrastructure as Code tools.
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Strong proficiency in Python.
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Experience in backend engineering, including APIs and services, as well as Generative AI, Machine Learning, or MLOps technologies such as Airflow, MLflow, pipelines, and monitoring tools.
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Solid understanding of CI/CD practices, containerization using Docker, and software engineering best practices.
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Familiarity with model deployment, model serving, and operating Machine Learning and AI systems in production environments.
Nice to have: Experience with observability, incident response, or platform operations.
