Our partner is looking for a MLOps Field Engineer based in Spain. 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 general Field Engineering team, you will collaborate closely with sales, product, engineering, and enterprise customers. 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.
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.
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.
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.
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.
Geographically adjusted compensation based on location, experience, and performance.
~ Performance-driven annual bonus or commission in addition to base compensation.
~ Personal learning and development budget of USD 2,000 per year.
~ 40 days of annual leave per year, including public holidays and company‑wide holiday periods.
~ Hands‑on exposure to AI/ML infrastructure, data processing pipelines, distributed training, Kubernetes, and emerging open source technologies.
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📌 Service Field Engineer (España)
🏢 Jobgether
📍 España