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Senior GCP DevOps Engineer (MLOps & GenAI)

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Dacodes: Senior GCP DevOps with MLOps & GenAI Specialization

Headquarters: Mexico

About the Role

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

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

Senior GCP DevOps Engineer (MLOps & GenAI)

100% Remote | LATAM

Are you passionate about GCP, Kubernetes, IaC, and want to work with AI/LLMs in production? This role is for you.

What We're Looking For

We are looking for someone who excels in:

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

And who also has experience or a strong interest in:

  • Vertex AI, MLflow
  • ML model deployment
  • LLMs, RAG, multi-agent workflows
  • Scalable AI systems

You will be the one enabling the infrastructure that brings AI to life in production.

Responsibilities

Infrastructure & DevOps (Core of the Role)

  • 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 branching, versioning, and deployment practices.

Kubernetes / GKE

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

MLOps

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

GenAI & Multi-Agent Systems

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

Observability & 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.

Mandatory Requirements

Base DevOps/Cloud (Most Important)

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

MLOps Specialization

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

GenAI Experience

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

⭐ Nice to Have

  • 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 expense 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.
  • Proudly certified as a Great Place to Work.

To Apply

https://weworkremotely.com/remote-jobs/dacodes-senior-gcp-devops-con-especializacion-en-mlops-genai

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