MLOps Field Engineer

Jobgether; España; Híbrido; EUR 65.000 - 95.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Learning budget USD 2,000 per year

40 days of annual leave

Travel opportunities

Wellness Platform

Travel upgrades for events

Descripción de la vacante

Jobgether is seeking an MLOps Field Engineer based in Spain to help organizations adopt AI/ML technologies across cloud environments. You will design and deliver ML and data architectures using Linux, Kubernetes, and open-source tools, combining technical consulting, solution architecture, and hands-on implementation.

You will translate customer requirements into scalable infrastructure solutions, contribute to product direction with feedback, and travel for customer meetings and events.

Formación

• Bachelor’s degree in a technical discipline or compelling alternative background.
• Experience in data engineering, MLOps, analytics, or deployment of big data solutions.
• Proficient in Python, R, or Rust with intermediate Python.
• Hands-on Linux, virtualization, containers, networking, and cloud computing concepts.
• Experience with Kubernetes and cloud platforms (AWS, Azure, Google Cloud).
• Understanding of enterprise open-source technologies, private clouds, ML, AI, data platforms, and analytics.
• Experience designing, deploying, or operating customer-facing technical solutions is valuable.
• Strong problem-solving, business-minded approach, and ability to translate challenges into practical solutions.
• Excellent written and spoken English with strong presentation and interpersonal skills.

Responsabilidades

• Architect cloud infrastructure and AI/ML solutions using Kubernetes, Kubeflow, OpenStack, Spark, and public clouds.
• Work across the Linux stack including networking, storage, virtualization, containers, and applications.
• Design and deliver solutions for on-premises and public clouds (AWS, Azure, Google Cloud).
• Gather requirements and recommend open-source technologies and infrastructure solutions.
• Deploy, test, validate, and hand over solutions to support/managed services teams.
• Deliver technical presentations, demonstrations, architecture discussions, and training sessions for customers.
• Collaborate with sales to develop solutions addressing customer needs and commercial objectives.
• Provide feedback to product/engineering based on customer experience and market needs.
• Contribute to engineering culture by sharing knowledge and supporting colleagues.
• Develop and maintain Python-based solutions and Kubernetes operators as needed.
• Travel internationally up to 30% of time for meetings and events.

Conocimientos

Python

Linux

Kubernetes

Cloud computing

English communication

Educación

Bachelor’s degree in technical discipline

Herramientas

Kubeflow

OpenStack

Spark

Debian/Ubuntu

Docker

Descripción del empleo

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a MLOps Field Engineer based in Spain.

Join a global Field Engineering team helping organizations adopt modern AI and machine learning technologies across public and private cloud environments. You will design and deliver sophisticated ML and data architectures using Linux, Kubernetes, and leading open-source technologies. The role combines technical consulting, solution architecture, customer engagement, and hands-on implementation rather than traditional software development. You will solve complex challenges involving distributed training, large-scale data processing, real-time analytics, and high-performance AI workloads. Working closely with sales and technical teams, you will translate customer requirements into scalable, practical infrastructure solutions. You will also influence product direction by sharing customer insights and technical feedback with engineering and product teams. This is an opportunity to work directly with customers while gaining hands-on exposure to emerging AI, MLOps, cloud, and open-source technologies.

Accountabilities

• Architect cloud infrastructure and AI/ML solutions using technologies such as Kubernetes, Kubeflow, OpenStack, Spark, and public cloud platforms.
• Work across the Linux technology stack, including networking, storage, infrastructure, virtualization, containers, and applications.
• Design and deliver solutions for both on-premises environments and public clouds such as AWS, Azure, and Google Cloud.
• Gather customer business and technical requirements and recommend appropriate open-source technologies and infrastructure solutions.
• Deploy, test, validate, and hand over technical solutions to support or managed services teams following project completion.
• Deliver technical presentations, demonstrations, architecture discussions, and training sessions for prospective and existing customers.
• Collaborate closely with sales teams to develop solutions that address customer requirements and contribute to shared commercial objectives.
• Provide technical feedback to product and engineering teams based on customer requirements, implementation experience, and emerging market needs.
• Contribute to a healthy, collaborative engineering culture by sharing knowledge, supporting colleagues, and promoting effective technical practices.
• Develop and maintain Python-based solutions and Kubernetes operators where required to support infrastructure and automation initiatives.
• Travel internationally for customer meetings, industry events, internal events, and project-related activities, with travel potentially reaching up to 30% of working time.

Requirements

• Bachelor’s degree in a technical discipline or a compelling alternative professional background.
• Experience in data engineering, MLOps, analytics, or deployment of big data solutions.
• Practical experience with a programming language such as Python, R, or Rust, with intermediate Python skills expected.
• Hands-on knowledge of Linux, virtualization, containers, networking, and cloud computing concepts.
• Experience with Kubernetes and familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Understanding of large-scale enterprise open-source technologies, private clouds, machine learning, AI, data platforms, and analytics.
• Experience designing, deploying, or operating technical solutions in customer-facing environments is valuable.
• Strong business-minded problem-solving skills and the ability to translate technical challenges into practical solutions.
• Excellent written and spoken English, with strong presentation and interpersonal communication skills.
• Confidence to exchange feedback, contribute ideas, challenge assumptions respectfully, and collaborate with diverse stakeholders.
• Strong curiosity, flexibility, accountability, self-motivation, and commitment to continuous learning.
• A proactive, results-oriented approach with the ability to manage commitments and adapt quickly to new projects.
• Passion for technology demonstrated through professional work, personal projects, or other technical initiatives.
• Familiarity with Linux, particularly Debian or Ubuntu, is preferred.
• Ability to work effectively within distributed, multicultural, and multinational teams.
• Willingness and ability to travel internationally, including for events lasting up to two weeks and customer or industry meetings.

Benefits

• Distributed work environment with twice-yearly in-person team sprints.
• Personal learning and development budget of USD 2,000 per year.
• Annual compensation review based on location, experience, and performance.
• Performance-driven annual bonus or commission.
• Recognition rewards.
• 40 days of annual leave per year, including public holidays and company-wide holiday periods.
• Maternity and paternity leave.
• Team Member Assistance Program and Wellness Platform.
• Opportunities to travel internationally and meet colleagues in different locations.
• Priority Pass and travel upgrades for long-haul company events.
• Hands-on exposure to AI/ML infrastructure, MLOps, Kubernetes, cloud platforms, data engineering, and open-source technologies.
• Opportunities to work directly with customers across different industries and solve complex, real-world technical challenges.
• Continuous learning opportunities across emerging technologies, distributed systems, data platforms, and AI infrastructure.