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Global Mlops Engineer: Scale Enterprise Ml Platform

Shakers
Barcelona, Spain
27 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience required
4 years minimum
Working hours
Regular working hours
Job source
www.buscojobs.com.es

Tech stack

Amazon Web Services Machine Learning Azure Machine Learning Kubernetes Machine Learning Operations Databricks

Job description

Shakers busca integrar un/a MLOps Engineer para liderar la evolución de una plataforma de Machine Learning en escala global.Consulte la descripción del puesto a continuación.Si confía en que tiene las habilidades y la experiencia adecuadas, envíe su solicitud hoy mismo.La misión incluye la construcción y mantenimiento de una infraestructura de ML robusta y escalable, así como el diseño de pipelines en Databricks y gestión de despliegues en Kubernetes.xqbhyrx Se requiere contar con 4-7 años de experiencia en MLOps o ML Platform Engineering, sólida experiencia en AWS, y conocimiento profundo de Kubernetes.Este rol ofrece la oportunidad de trabajar en un entorno técnico senior y tener impacto global desde el primer día.#J-*****-Ljbffr

Requirements

xqbhyrx Se requiere contar con 4-7 años de experiencia en MLOps o ML Platform Engineering, sólida experiencia en AWS, y conocimiento profundo de Kubernetes.Este rol ofrece la oportunidad de trabajar en un entorno técnico senior y tener impacto global desde el primer día.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.buscojobs.com.es

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:44 min

Defining core roles and responsibilities in MLOps teams

Bas Geerdink · LIVE

2:28 min

Understanding Kubernetes architecture and core cluster components

Marc Nimmerrichter · WWC 2022

2:27 min

Managing traffic and tracking costs with Databricks Unity Catalog

Viktoria Semaan Viktoria Semaan · WWC Europe 2026

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · WWC 2023

4:04 min

Overview of Kubernetes operators and custom resource definitions

Philipp Krenn · WWC 2022

Videos

See all
Effective Machine Learning - Managing Complexity with MLOps thumbnail
45:45
WWC 2021 • about 5 years ago

Effective Machine Learning - Managing Complexity with MLOps

Simon Stiebellehner

Introduction to Azure Machine Learning thumbnail
54:27
LIVE • over 4 years ago

Introduction to Azure Machine Learning

Jose Luis Latorre Millas

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source thumbnail
29:50
WWC Europe 2026 • about 1 month ago

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source

Viktoria Semaan

Understanding Kubernetes in a visual way thumbnail
29:05
WWC Europe 2026 • about 1 month ago

Understanding Kubernetes in a visual way

Aurélie Vache

DevOps for Machine Learning thumbnail
44:40
WWC 2021 • about 5 years ago

DevOps for Machine Learning

Hauke Brammer

The state of MLOps - machine learning in production at enterprise scale thumbnail
47:19
LIVE • over 4 years ago

The state of MLOps - machine learning in production at enterprise scale

Bas Geerdink

Related articles

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