Python and Kubernetes Software Engineer - Data, AI / ML & Analytics

Overview

Python and Kubernetes Software Engineer - Data, AI / ML & Analytics at Canonical. Location : Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia. Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is widely used in cloud, data science, AI, engineering innovation and IoT. We hire distributedly with teams in multiple time zones and most colleagues work remotely.

As a software engineer on the team, you will collaborate on an end-to-end data analytics and MLOps solution built with open-source tools such as Kubeflow, MLFlow, DVC, Feast, Apache Superset, dbt, Temporal, Apache Trino, and ClickHouse. The team owns a solution from analytics and machine learning space and may run on servers, cloud, Kubernetes, or as web services.

Responsibilities

• Develop understanding of the Linux stack from kernel to application layer
• Design, build and maintain solutions deployed on public / private clouds and local workstations
• Master distributed systems concepts such as observability, identity, tracing
• Work with Kubernetes and machine-oriented open-source applications
• Collaborate with a distributed team of engineers, designers and product managers
• Debug issues and interact with upstream and Ubuntu communities
• Generate ideas and collaborate on finding good solutions

What we are looking for

Additional Skills That Would Be Nice To Have

What we offer

About Canonical

Canonical is a pioneering tech firm at the forefront of the open source movement. We publish Ubuntu and enable the global move to AI, IoT and cloud. We recruit on a global basis and value excellence. Most colleagues have worked from home since 2004. We are an equal opportunity employer and welcome diverse applicants.

Equal Opportunity

Canonical is an equal opportunity employer. We are proud to foster a workplace free from discrimination. Whatever your identity, we will give your application fair consideration.

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