[EC-92] | MLOps Engineer
hace 3 semanas
En WayOps buscamos un perfil MLOps Engineer que quiera desarrollar su carrera profesional formando parte de un equipo Data & AI de primer nivel y trabajando en proyectos cloud con las últimas tecnologías.CONTEXTO & RESPONSABILIDADESLa persona seleccionada se incorporará dentro de un equipo de nueva formación que tendrá como misión automatizar mediante MLOps la creación responsable de modelos en la plataforma analítica.Junto al AI Architect, el MLOps Engineer deberá implementar pipelines CI / CD que permitan la integración y entrega continuas de código (DevOps) y modelos (ModelOps).Será imprescindible contar con un background técnico en programación y familiaridad tanto con DevOps, como con el ciclo de vida de los modelos científicos.PROYECTO & EQUIPOEl proyecto persigue la adaptación de la plataforma analítica existente para integrar la creación responsable de modelos y automatizar su despliegue mediante MLOps.Como quiera que la base tecnológica ya incluye Azure Databricks y Azure Machine Learning Services, se quiere realizar la implementación del proceso gobernado desde Azure DevOps integrando a través de SDK con el resto de servicios para la creación automática de recursos o implementar las pipelines de despliegue.Para el éxito del proyecto, resulta imprescindible contar con especialistas que además de tener visión de la arquitectura puedan realizar la configuración de las pipelines y crear los arquetipos en código que integren con los diferentes servicios de Azure Machine Learning.La configuración de cada iniciativa debe permitir el desarrollo local o remoto contra un clúster en Databricks o un Compute en Azure Machine Learning. Toda la configuración del proyecto y despliegue deben ser automatizados.El equipo designado para el proyecto incluirá ingenieros de automatización MLOps (MLOps Engineer) e ingenieros de industrialización IA (AI Engineer) que serán supervisados por el líder técnico (Team Lead). El AI Architect trabajará de la mano del líder técnico y deberá liderar las tareas del equipo, especialmente los ingenieros de industrialización AI. Además, el proyecto contará con la supervisión del arquitecto empresarial y el apoyo del especialista de la plataforma. En total el equipo del proyecto serán unas ocho personas.EXPERIENCIA & CONOCIMIENTOSEl perfil a incorporar deberá contar con 2-3 años de experiencia como especialista DevOps liderando la creación de pipelines de integración y despliegue continuo con Azure DevOps, así como código que permita la industrialización y automatización del despliegue de entornos (IaC). Además, deberá contar con un background de 3-5 años de experiencia como Ingeniero de Software o Ingeniero de Sistemas desarrollando aplicaciones y programas o desarrollando scripts que faciliten la configuración de entornos.Se valorará experiencia previa en industrialización de entornos Data & AI, especialmente los orientados al ciclo de vida de los modelos científicos. Además, se valorará experiencia con Bicep / Terraform y experiencia o conocimiento con la API de Azure Databricks.Será necesario tener experiencia previa con tecnologías :Azure DevOps (Boards, Pipelines, Repos, Test Plans, Artifacts)Azure (Key Vault, Managed Identities, Application Insights, Azure Monitor, App Service, Storage)Azure Machine Learning (Experiment Tracking, Model Registry, AML SDK v2, MLflow)IaC (Terraform, Databricks API, Bicep, Docker, Powershell, Scripting)QA & Testing (Kiuwan, JMeter, PyTest)Visual Studio Code, Git, GitFlowAdemás se valorará positivamente contar con experiencia o conocimientos en :Azure (Data Factory, Databricks, Azure Machine Learning, Cosmos DB, SQL Databases)Azure Machine Learning (AML Pipelines, AML Endpoints, AML Environments, AML Compute)Databricks (Delta Tables, Unity Catalog, Databricks Connect)Desarrollo Python (Click, Poetry, Pipx, Opencensus, Black, Pdb+, fastAPI)CONTRATACIÓN & UBICACIÓNLa contratación será mediante contrato anual prorrogable como autónomo en jornada completa. El trabajo se desarrollará en remoto preferentemente dentro del horario de oficina del cliente para facilitar la coordinación con el resto del equipo. Banda salarial negociable en función de la experiencia aportada. Incorporación inmediata.
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