{"@context":"https://schema.org","@type":"JobPosting","title":"MLOps Field Engineer","description":"

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.\n\n

This role is ideal for an MLOps or cloud engineering professional who enjoys solving complex technical challenges directly with customers. You will design and deliver modern AI/ML architectures using open source technologies, Linux, Kubernetes, and public or private cloud infrastructure. The position combines technical consulting, solution architecture, hands-on implementation, and customer engagement rather than traditional software development. You will work on large-scale challenges involving distributed machine learning, real-time data processing, hybrid cloud environments, and advanced analytics. As part of a global Field Engineering team, you will collaborate closely with sales, product, engineering, and enterprise customers. The role also provides exposure to emerging technologies and opportunities to influence technical roadmaps through real-world customer insights.

Accountabilities\n\n

Design and architect AI/ML, MLOps, data engineering, and cloud infrastructure solutions aligned with customer workloads and business requirements.Work across the Linux technology stack, including networking, storage, containers, applications, and infrastructure.Architect and deploy solutions using Kubernetes, Kubeflow, OpenStack, Spark, and related open source technologies.Deliver solutions across on-premises environments and major public cloud platforms, including AWS, Azure, and Google Cloud.Engage directly with customers to understand technical and business requirements and recommend appropriate open source solutions.Deploy, test, troubleshoot, and validate technical solutions before handing them over to support or managed services teams.Develop infrastructure automation and Kubernetes capabilities using Python and other relevant technologies.Deliver technical presentations, demonstrations, workshops, and training sessions covering cloud, Linux, AI/ML, and open source technologies.Collaborate closely with enterprise sales teams to support customer engagements, develop opportunities, and achieve shared commercial objectives.Work with product and engineering teams to communicate customer requirements, provide technical feedback, and influence product and technology roadmaps.Contribute to a collaborative engineering culture and share knowledge across a globally distributed technical community.Travel internationally for customer engagements, industry events, internal events, and project-related activities, with travel potentially reaching 30% of working time.\n\n

Design and architect AI/ML, MLOps, data engineering, and cloud infrastructure solutions aligned with customer workloads and business requirements.

Work across the Linux technology stack, including networking, storage, containers, applications, and infrastructure.

Architect and deploy solutions using Kubernetes, Kubeflow, OpenStack, Spark, and related open source technologies.

Deliver solutions across on-premises environments and major public cloud platforms, including AWS, Azure, and Google Cloud.

Engage directly with customers to understand technical and business requirements and recommend appropriate open source solutions.

Deploy, test, troubleshoot, and validate technical solutions before handing them over to support or managed services teams.

Develop infrastructure automation and Kubernetes capabilities using Python and other relevant technologies.

Deliver technical presentations, demonstrations, workshops, and training sessions covering cloud, Linux, AI/ML, and open source technologies.

Collaborate closely with enterprise sales teams to support customer engagements, develop opportunities, and achieve shared commercial objectives.

Work with product and engineering teams to communicate customer requirements, provide technical feedback, and influence product and technology roadmaps.

Contribute to a collaborative engineering culture and share knowledge across a globally distributed technical community.

Travel internationally for customer engagements, industry events, internal events, and project-related activities, with travel potentially reaching 30% of working time.\n\n

Requirements\n\n

Professional experience in MLOps, data engineering, big data, cloud infrastructure, or the deployment of machine learning and analytics solutions.Practical experience with Linux, virtualization, containers, networking, and cloud infrastructure.Experience with Kubernetes and familiarity with cloud platforms such as AWS, Azure, or Google Cloud.Working knowledge of MLOps, AI/ML infrastructure, data processing pipelines, distributed systems, or large-scale analytics environments.Intermediate Python programming skills, with experience in another language such as R or Rust considered an advantage.Understanding of open source technologies and an interest in enterprise applications of private cloud, machine learning, AI, data, and analytics.Ability to design technical architectures and translate complex customer requirements into practical infrastructure and solution designs.Strong customer-facing communication skills, with the ability to explain technical concepts through presentations, demonstrations, workshops, and discussions.Business-minded approach with the ability to balance technical quality, customer needs, and commercial objectives.Demonstrated problem-solving ability, initiative, and willingness to take ownership of complex projects.Strong interpersonal skills, curiosity, flexibility, accountability, and a results-oriented mindset.Confidence to exchange feedback, challenge ideas respectfully, and contribute actively to technical discussions.Passion for technology demonstrated through personal projects, continuous learning, open source involvement, or technical initiatives.Strong written and spoken English with excellent presentation skills.A technical undergraduate degree or a compelling alternative educational or professional background.Ability to work effectively with colleagues and customers across multicultural, multinational, and distributed environments.Willingness and ability to travel internationally for customer meetings, industry events, and company gatherings.\n\n

Professional experience in MLOps, data engineering, big data, cloud infrastructure, or the deployment of machine learning and analytics solutions.

Practical experience with Linux, virtualization, containers, networking, and cloud infrastructure.

Experience with Kubernetes and familiarity with cloud platforms such as AWS, Azure, or Google Cloud.

Working knowledge of MLOps, AI/ML infrastructure, data processing pipelines, distributed systems, or large-scale analytics environments.

Intermediate Python programming skills, with experience in another language such as R or Rust considered an advantage.

Understanding of open source technologies and an interest in enterprise applications of private cloud, machine learning, AI, data, and analytics.

Ability to design technical architectures and translate complex customer requirements into practical infrastructure and solution designs.

Strong customer-facing communication skills, with the ability to explain technical concepts through presentations, demonstrations, workshops, and discussions.

Business-minded approach with the ability to balance technical quality, customer needs, and commercial objectives.

Demonstrated problem-solving ability, initiative, and willingness to take ownership of complex projects.

Strong interpersonal skills, curiosity, flexibility, accountability, and a results-oriented mindset.

Confidence to exchange feedback, challenge ideas respectfully, and contribute actively to technical discussions.

Passion for technology demonstrated through personal projects, continuous learning, open source involvement, or technical initiatives.

Strong written and spoken English with excellent presentation skills.

A technical undergraduate degree or a compelling alternative educational or professional background.

Ability to work effectively with colleagues and customers across multicultural, multinational, and distributed environments.

Willingness and ability to travel internationally for customer meetings, industry events, and company gatherings.\n\n

Benefits\n\n

Geographically adjusted compensation based on location, experience, and performance.Performance-driven annual bonus or commission in addition to base compensation.Fully distributed work environment with twice-yearly in-person team sprints.Personal learning and development budget of USD 2,000 per year.Annual compensation review.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 collaborate with colleagues in different locations.Priority Pass access and travel upgrades for eligible long-haul company events.Hands-on exposure to AI/ML infrastructure, data processing pipelines, distributed training, Kubernetes, and emerging open source technologies.Opportunities to work directly with customers across a wide range of industries and technical environments.\n\n

Geographically adjusted compensation based on location, experience, and performance.

Performance-driven annual bonus or commission in addition to base compensation.

Fully distributed work environment with twice-yearly in-person team sprints.

Personal learning and development budget of USD 2,000 per year.

Annual compensation review.

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 collaborate with colleagues in different locations.

Priority Pass access and travel upgrades for eligible long-haul company events.

Hands-on exposure to AI/ML infrastructure, data processing pipelines, distributed training, Kubernetes, and emerging open source technologies.

Opportunities to work directly with customers across a wide range of industries and technical environments.\n\n

How Jobgether Works\n\n

We use an

AI-powered matching process

to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?\n\n

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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