MLE Developer with GCP, Shared Services Platform

We are looking for a hands on Middle MLE Infrastructure Engineer to strengthen a shared Data and AI platform. You will focus on execution across CI/CD standards, cloud resource management, identity and access security, observability, and reliable deployments that support other teams.

The Shared Services Platform organization builds and operates the core environment for Data and AI products. You will work on a platform that standardizes CI/CD, security, observability, and cloud resource management so product squads can deliver on reliable infrastructure.

• 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 through 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 Engineer, DevOps Engineer, or MLOps Infrastructure Engineer.
• Experience with infrastructure as code using Terraform and configuration automation.
• Experience with Docker, Kubernetes, and autoscaling.
• Experience with CI CD tooling such as GitHub Actions and GitLab CI.
• Experience with 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 solutions.
• Experience with IAM, vulnerability scanning, data masking, and policy management.
• Ability to communicate effectively with technical and non technical stakeholders.
• Experience working in Agile environments.
• 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 environments.
• Familiarity with GenAI tooling such as LangChain and Agents.