MLE Infrastructure Engineer

We are looking for a hands on MLE Infrastructure Engineer to strengthen the shared Data and AI platform. You will help define and maintain delivery standards, manage cloud infrastructure and cost, improve security and observability, and support reliable deployments across teams.

Airline Platform is a shared services initiative that supports Data and AI products with reliable and standardized infrastructure. You will work on infrastructure as code, container orchestration, security automation, and platform reliability in an Agile and DataOps environment.

• Define and maintain homogeneous CI CD standards and pipelines for data and AI across teams
• Manage cloud resources, capacity, and cost through provisioning, autoscaling, and optimization
• Own identity and access management and centralized credential governance
• Automate security policies through infrastructure as code and SecOps practices
• Run vulnerability scanning and incident response playbooks
• Build observability with metrics, dashboards, and proactive alerting for platform and products
• Ensure reliable versioning and deployment standards across the platform
• Act as a platform enabler for product squads on environment and product infrastructure

• Proven experience as a Platform, DevOps, or MLOps infrastructure engineer
• Experience with infrastructure as code using Terraform and configuration automation
• Experience with Docker, Kubernetes, and autoscaling
• Experience with GitHub Actions, GitLab CI, and Git workflows including GitFlow
• Experience with Google Cloud Platform resource management, GCP IAM, billing, and cost control
• Experience with observability tools such as Prometheus, Grafana, Datadog, or similar monitoring tools
• Experience with IAM, vulnerability scanning, data masking, and policy management
• Ability to communicate effectively with technical and non technical stakeholders
• Agile experience
• Fluency in Spanish

• Exposure to MLOps platforms, model registry, and model serving infrastructure
• Experience with FastAPI or Flask
• Familiarity with BigQuery, pipelines, and lakehouse or warehouse concepts
• Familiarity with GenAI tooling such as LangChain or Agents