Senior GCP DevOps with MLOps & GenAI Specialization

We are looking for a Senior GCP DevOps Engineer with a deep understanding of infrastructure in Google Cloud, automation, Kubernetes, Terraform and CI/CD; who also has experience or specialization in MLOps and GenAI, to enable and operate AI platforms based on Machine Learning and LLM models.

This role is key to ensuring that the models, workflows and multi-agent systems of the AI team can be executed in a scalable, reliable, secure and efficient manner.

100% remote | LATAM
Are you passionate about GCP, Kubernetes, IaC and want to work with AI/LLM models in production?
This role is for you.

GCP (IAM, VPCs, Cloud Run, Compute Engine, Pub/Sub…)
Kubernetes/GKE (even better if you have worked with GPUs)
Advanced Terraform
GitLab CI/CD
Observability / costs / security

And who also has experience or strong interest in:
Vertex AI, MLflow
ML model deployment
LLMs, RAG, multi-agent workflows
Scalable AI systems

You will be the one who enables the infrastructure that allows AI to come to life in production.

Requirements

• Design, automate and operate infrastructure in GCP (IAM, networks, VPCs, Cloud Run, Compute Engine, Pub/Sub, Cloud SQL).
• Implement Infrastructure as Code practices using Terraform (modules, remote state, multi-environment workspaces).
• Build and maintain CI/CD pipelines with GitLab, ensuring good practices for branching, versioning and deployment.

• Manage clusters in GKE, including GPU nodepools, autoscaling, security, networking and monitoring.
• Deploy AI/ML applications and inference services on GKE or Cloud Run.

• Integrate and operate Machine Learning platforms like Vertex AI, MLflow or equivalents.
• Deploy models in online endpoints, batch jobs or containers.
• Manage experiment tracking, model registry and artifacts.

• Consume LLM APIs (GPT, Gemini, Claude, etc.).
• Implement workflows with RAG, embeddings, multi-agent steps or concurrency pipelines.
• Deploy LLM-based services on GCP, optimizing performance and costs.

• Configure monitoring and traceability (Grafana, Datadog, Looker Studio).
• Monitor LLM token consumption, GPU/CPU resources and GCP costs.
• Implement latency, failure and load alerts.

• +4 years of experience with GCP in production.
• +3 years with advanced Terraform.
• +3 years administering Kubernetes/GKE, ideally with GPU.
• +3 years building CI/CD pipelines.
• Mastery of Docker, cloud security, networks and observability.

• Have collaborated with data/AI squads (you don't have to be the one training models, but you have deployed ML models or services).
• Experience deploying ML models in batch or online endpoints.
• Some experience with GenAI: LLMs, RAG or at least API consumption (OpenAI, Gemini, etc.).
• Vertex AI / MLflow / SageMaker / Azure ML (any applicable).
• Knowledge of experiment tracking and model versioning.

• Use of LLM APIs.
• Familiarity with RAG or multi-agent workflows.
• Understanding of tokens, latency, concurrency and costs in inference.

• GCP certification (Cloud Architect, Data Engineer or ML Engineer).
• Experience with Dataflow, BigQuery or data pipelines.
• Knowledge of NLP or frameworks like LangChain, LangGraph, LlamaIndex.

Benefits

Integration into global brands and disruptive startups.

Remote work/Home office.

In case of requiring a hybrid or in-person modality, you will be informed from the first session.

⏳ Schedule adjusted to the work cell/assigned project.

Work from Monday to Friday.

Day off on your birthday.

Major medical expenses insurance (applies to Mexico).

️ Life insurance (applies to Mexico).

Multicultural work teams.

Access to courses and certifications.

Meetups with special guests from the IT area.

Virtual integration events and interest groups.

English classes.

Opportunities within our different lines of business.

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