MLOps Field Engineer

Accountabilities:

• 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.

Requirements

• 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.

• 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.

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