Senior GCP DevOps con Especialización en MLOps & GenAI job opportunity at DaCodes.

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Date2025-12-03T19:40:09.437Z bot
DaCodes Senior GCP DevOps con Especialización en MLOps & GenAI
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Pattern: full-time
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🧭 Descripción General Buscamos un/a Senior GCP DevOps Engineer con profundo dominio de infraestructura en Google Cloud, automatización, Kubernetes, Terraform y CI/CD; que además cuente con experiencia o especialización en MLOps y GenAI , para habilitar y operar plataformas de IA basadas en modelos de Machine Learning y LLMs. Este rol es clave para garantizar que los modelos, workflows y sistemas multiagente del equipo de IA puedan ejecutarse de forma escalable, confiable, segura y eficiente . Senior GCP DevOps Engineer (MLOps & GenAI) 100% remoto | LATAM ¿Te apasiona GCP, Kubernetes, IaC y quieres trabajar con modelos de IA/LLMs en producción? Este rol es para ti. Buscamos a alguien que domine: 🔥 GCP (IAM, VPCs, Cloud Run, Compute Engine, Pub/Sub…) 🔥 Kubernetes/GKE (mejor aún si has trabajado con GPU) 🔥 Terraform avanzado 🔥 GitLab CI/CD 🔥 Observabilidad / costos / seguridad Y que además tenga experiencia o interés fuerte en: 🤖 Vertex AI, MLflow 🤖 Despliegue de modelos ML 🤖 LLMs, RAG, workflows multiagente 🤖 Sistemas de IA escalables Serás quien habilite la infraestructura que permite que la IA cobre vida en producción. 🔧 Responsabilidades Principales Infraestructura & DevOps (Core del rol) Diseñar, automatizar y operar infraestructura en GCP (IAM, redes, VPCs, Cloud Run, Compute Engine, Pub/Sub, Cloud SQL). Implementar prácticas de Infraestructura como Código usando Terraform (módulos, state remoto, workspaces multiambiente). Construir y mantener pipelines CI/CD con GitLab , asegurando buenas prácticas de branching, versionado y despliegue. Kubernetes / GKE Administrar clústeres en GKE , incluyendo nodepools con GPU, autoscaling, seguridad, networking y monitoreo. Desplegar aplicaciones de IA/ML y servicios de inferencia en GKE o Cloud Run. MLOps Integrar y operar plataformas de Machine Learning como Vertex AI, MLflow o equivalentes . Desplegar modelos en endpoints online, batch jobs o contenedores. Gestionar experiment tracking, model registry y artefactos. GenAI & Sistemas Multiagente Consumir APIs de LLMs (GPT, Gemini, Claude, etc.). Implementar workflows con RAG, embeddings, pasos multiagente o pipelines de concurrencia . Desplegar servicios basados en LLM en GCP, optimizando rendimiento y costos. Observabilidad & Costos Configurar monitoreo y trazabilidad (Grafana, Datadog, Looker Studio). Monitorear consumo de tokens de LLMs, recursos de GPU/CPU y costos de GCP. Implementar alertas de latencia, fallas y carga. 🧩 Requisitos Obligatorios Base DevOps/Cloud (lo más importante) +4 años de experiencia con GCP en producción. +3 años con Terraform avanzado. +3 años administrando Kubernetes/GKE , idealmente con GPU. +3 años construyendo pipelines CI/CD . Dominio de Docker, seguridad en cloud, redes y observabilidad. Especialización MLOps Haber colaborado con squads de datos/IA (no hace falta que sea el que entrena modelos, pero sí que haya desplegado modelos o servicios de ML). Experiencia desplegando modelos ML en endpoints batch u online. Alguna experiencia con GenAI: LLMs, RAG o al menos consumo de APIs (OpenAI, Gemini, etc.). Vertex AI / MLflow / SageMaker / Azure ML (cualquiera aplicable). Conocimientos de experiment tracking y versionado de modelos. Experiencia en GenAI Uso de LLM APIs. Familiaridad con RAG o workflows multiagente. Comprensión de tokens, latencia, concurrencia y costos en inferencia. ⭐ Nice to Have Certificación GCP (Cloud Architect, Data Engineer o ML Engineer). Experiencia con Dataflow, BigQuery o pipelines de datos. Conocimientos en NLP o frameworks como LangChain, LangGraph, LlamaIndex.

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