MLOps Field Engineer at Canonical

Role overview

This field engineering role helps enterprises design AI and machine-learning infrastructure during the presales process. It centres on customer requirements, architecture, demonstrations and deployment rather than internal model research. Canonical describes home-based teams across EMEA, the Americas and APAC, with international travel up to 30% for customer and company events.

Responsibilities

• Gather business and technical requirements and advise customers on Ubuntu and open-source AI/ML software.
• Architect and deploy data-processing, ML and cloud infrastructure using Kubernetes, Kubeflow, OpenStack, Spark and related tools.
• Deliver solutions on premises or on AWS, Azure and Google Cloud, then test and hand them to support or managed services.
• Present Ubuntu Pro and AI/ML demonstrations to prospective and existing customers.
• Work with sales leads, relay customer requirements to product teams and support shared commercial goals.

Qualifications

• Experience deploying data engineering, MLOps or big-data solutions, plus programming in a language such as Python, R or Rust.
• Practical Linux, virtualisation, containers, networking and cloud knowledge; Kubernetes and AWS, Azure or Google Cloud are relevant.
• Business-minded problem solving and professional English presentation skills for customer conversations.
• Familiarity with machine-learning infrastructure, private clouds and analytics applications.
• Technical degree or a convincing alternative path, strong record of learning and ability to travel for customer and company meetings.

Technology in this role

• Python: Python AWS Azure GCP Kubernetes SQL .
• AWS: Python AWS Azure GCP Kubernetes SQL .
• Azure: Python AWS Azure GCP Kubernetes SQL .
• GCP: Python AWS Azure GCP Kubernetes SQL .
• Kubernetes: Python AWS Azure GCP Kubernetes SQL .
• SQL: Python AWS Azure GCP Kubernetes SQL .
• Linux: Python AWS Azure GCP Kubernetes SQL .
• Rust: Experience deploying data engineering, MLOps or big-data solutions, plus programming in a language such as Python, R or .
• R: Experience deploying data engineering, MLOps or big-data solutions, plus programming in a language such as Python, R or .
• Spark: Architect and deploy data-processing, ML and cloud infrastructure using Kubernetes, Kubeflow, OpenStack, Spark and related .
• Google Cloud: Deliver solutions on premises or on AWS, Azure and Google Cloud, then test and hand them to support or managed .
• CAN: We are growing teams in EMEA, Americas and APAC time zones, so can accommodate candidates from almost any .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This MLOps Field Engineer position at Canonical is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “This field engineering role helps enterprises design AI and machine-learning infrastructure during the presales process. It centres on customer requirements, architecture, demonstrations and deployment rather than…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the technical area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Gather business and technical requirements and advise customers on Ubuntu and open-source AI/ML software.”; “Architect and deploy data-processing, ML and cloud infrastructure using Kubernetes, Kubeflow, OpenStack, Spark and related tools.”; “Deliver solutions on premises or on AWS, Azure and Google Cloud, then test and hand them to support or managed services.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Experience deploying.

Benefits

• We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognize outstanding performance. In addition to base pay, we offer a performance-driven annual bonus or commission. We provide all team members with additional benefits which reflect our values and ideals. We balance our programs to meet local needs and ensure fairness globally.
• Distributed work environment with twice-yearly team sprints in person
• Personal learning and development budget of USD 2,000 per year
• 40 days annual leave per annum, including public holidays and our All Company holiday periods
• Team Member Assistance Program & Wellness Platform
• Opportunity to travel to new locations to meet colleagues
• Priority Pass and travel upgrades for long-haul company events

About Canonical

Canonical publishes Ubuntu and develops open-source products for cloud, AI, connected devices and enterprise infrastructure. It describes most engineering roles as home based, with periodic in-person team gatherings.

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