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

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Job Overview

The Python and Kubernetes Software Engineer - Data, AI/ML & Analytics at Canonical will play a pivotal role in designing and implementing robust data solutions that leverage AI/ML technologies. The candidate will be responsible for developing software solutions, utilizing Kubernetes for scalable deployments, and ensuring that data pipelines are efficient and reliable. This position requires a strong emphasis on collaboration with cross-functional teams to transform analytics requirements into actionable software products, ultimately contributing to diverse projects that enhance user experiences and drive innovation within the data landscape.

Job Responsibilities

• Design and implement Python-based software applications focusing on data processing and analytics.
• Develop and maintain Kubernetes clusters for deploying AI/ML applications and data pipelines.
• Collaborate with data scientists and engineers to transition machine learning models from prototype to production.
• Optimize data workflows and ensure the reliability of data processing systems.
• Participate in code reviews and maintain high standards of coding best practices.
• Monitor and troubleshoot production systems, ensuring optimal performance and availability.
• Contribute to the documentation of processes, software designs, and data architectures.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Proven experience in software development using Python.
• Hands-on experience with Kubernetes and container orchestration.
• Understanding of AI/ML concepts and frameworks such as TensorFlow or PyTorch.
• Experience with data processing tools, such as Apache Spark or Apache Kafka.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.

Technologies & Tools

The position primarily involves a robust set of technologies and tools, including Python for software development, Kubernetes for container orchestration, and various data processing frameworks such as Apache Spark. Familiarity with machine learning libraries like TensorFlow or PyTorch is essential. The candidate should also be adept at using version control systems such as Git, as well as monitoring tools for performance and system health.

Ideal Candidates

Ideal candidates for this position are those who possess a strong technical foundation combined with a passion for data and analytics. They should demonstrate a proactive approach to problem-solving, be committed to producing high-quality work, and have the ability to thrive in a dynamic, collaborative environment. Strong interpersonal skills and a desire to continuously learn and adapt to new technologies will also contribute to their success in this role.