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MLOps

Q-tech

Role overview

Qualifications

  • Solid experience in ML production environments
  • Strong background in engineering (software or infrastructure)
  • Experience with cloud (AWS) and distributed systems
  • Hands-on profile with the ability to define standards

Responsibilities

  • Design and evolve the ML Platform (pipelines, standards, tooling)
  • Facilitate the end-to-end cycle of models: training, validation, and deployment
  • Implement engineering best practices (CI/CD, testing, monitoring)
  • Work with real-time systems (streaming with Apache Kafka)

About the company

Q-tech logo

Q-tech

Q-tech is a Recruitment Company specialising in the Information Technology field. Throughout the years, we have been able to build up a network of contacts and a very large database, which serve us as indispensable sources when it comes to identifying and contacting professionals of interest to our clients. Q-tech can call on a great deal of experience in finding and selecting all types of profiles in the Information Technology sector: Web/Software Analyst/Developer/Programmer/Engineer/Architect/Craftsman: - JAVA (J2EE, Spring, Hibernate) - C/C++, Go, Ruby (on Rails), Grails, Python (Django), Erlang, Bash, Perl - .NET (C#, VB.NET, Core, WEB Api, WPF, WCF) - PHP (Symfony, Laravel) - JavaScript (ES6, React, Redux, Angular, Node, Express, Ember, Backbone, Underscore, Typescript) - Mobile: Android (Kotlin, Java), iOS (Swift, Objective-C), React Native, Ionic, Cordova - Functional languages: Scala, Clojure, Haskell - Microservices Architecture, RESTful APIs, MVC, Object Orientated, SOLID, Design Patterns, TDD/DDD/BDD, Continuous Integration/Delivery, Distributed Systems, High Availability, High Traffic, High Performance, Algorithms, Data-structures, Asynchronous Programming, Agile, SCRUM CTO, Software Development Manager Data Engineers, Data Scientist, (NLP, Machine Learning, Deep Learning, Predictive Analytics) System/Database Administrator: - DBA's (Oracle, SQL, MySQL, PostgreSQL, NoSQL, Cassandra, Zookeeper, MongoDB, Couchbase) - Big Data (Spark, Kafka, AKKA, Hadoop, Elasticsearch) - System Administrator (Linux, Windows, Mac) and DevOps (Apache, Nginx, Docker, AWS, Azure, Kubernetes, Ansible, Puppet, Chef, Salt) - Network Engineer and Helpdesk/Service Desk/Support - QA and Testing (Selenium, Cucumber), Test/Task Automation - Security Engineer, Ethical Hacker, Pentester, SIEM - Product Owner, Product Manager, UI/UX - SAP, CRM, ERP - Business Analyst, Project Manager - Cloud Computing (AWS, Azure), VMware - Business Intelligence, Data Warehouse, Data Analysis - SEO, SEM specialists

Company details

Company size11 - 50

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Job description

Desde q-tech buscamos un MLOps / Senior ML Engineer para un importante marketplace tecnológico en pleno crecimiento, con millones de usuarios y un volumen de datos muy elevado.

La compañía se encuentra en un momento clave de evolución de su área de Data & Machine Learning, y busca incorporar a una persona que lidere la construcción y evolución de su ML Platform.

Sobre el rol

No es una posición orientada al desarrollo de modelos, sino a construir toda la infraestructura y tooling que permite a los Data Scientists trabajar de forma eficiente y llevar modelos a producción de forma escalable y fiable.

Serás una figura clave, con alto nivel de ownership, ya que hasta ahora estas responsabilidades estaban distribuidas entre distintos equipos.

Responsabilidades

  • Diseñar y evolucionar la ML Platform (pipelines, estándares, tooling)

  • Facilitar el ciclo end-to-end de modelos: entrenamiento, validación y despliegue

  • Implementar buenas prácticas de ingeniería (CI/CD, testing, monitorización)

  • Trabajar con sistemas en tiempo real (streaming con Apache Kafka)

  • Escalar infraestructura en AWS (incluyendo AWS SageMaker, S3, etc.)

  • Orquestar workloads mediante Kubernetes

  • Colaborar estrechamente con Data Scientists, Data Engineers y equipos de Platform

Stack tecnológico

AWS, Kubernetes, Kafka, Airflow, Python, CI/CD, herramientas de MLOps

Qué buscamos

  • Experiencia sólida en entornos de ML en producción

  • Background fuerte en ingeniería (software o infraestructura)

  • Experiencia con cloud (AWS) y sistemas distribuidos

  • Mentalidad de buenas prácticas: testing, automatización, escalabilidad

  • Perfil hands-on con capacidad de definir estándares

Qué ofrecen

  • Proyecto con mucho impacto y margen de construcción

  • Rol estratégico dentro del equipo de Data & ML

  • Entorno técnico exigente y colaborativo

  • Modelo híbrido en Barcelona

Si buscas un rol donde realmente puedas construir y liderar la capa de MLOps desde dentro, este puede ser un muy buen siguiente paso 🚀

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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