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

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Job Overview

As a Python and Kubernetes Software Engineer specializing in Data, AI/ML, and Analytics at Canonical, you will be responsible for developing and deploying advanced software solutions that facilitate innovative analytics and machine learning applications. You will work closely with cross-functional teams to design systems that efficiently manage data pipelines, optimize cloud-based resources, and ensure the robustness of AI solutions using Kubernetes environments. Your expertise in Python programming and container orchestration will be pivotal in driving the delivery of high-performance applications that leverage data analytics capabilities.

Job Responsibilities

• Design, develop, and deploy Python applications for data processing and analytics.
• Implement and manage Kubernetes clusters for orchestrating machine learning workloads.
• Enhance existing data pipelines and ensure data quality and integrity throughout.
• Collaborate with data scientists to integrate and optimize machine learning models.
• Monitor application performance and troubleshoot issues in production environments.
• Contribute to the development of best practices for system design and coding standards.
• Stay updated with emerging technologies in AI/ML and cloud infrastructure.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Proven experience in Python development, especially for data-intensive applications.
• Strong knowledge of Kubernetes and containerization technologies.
• Experience with machine learning libraries such as TensorFlow or PyTorch is preferred.
• Familiarity with data pipeline tools such as Apache Kafka, Apache Airflow, or similar.
• Solid understanding of cloud environments (AWS, Azure, GCP) and DevOps practices.
• Excellent problem-solving skills and a strong attention to detail.

Technologies & Tools

The Python and Kubernetes Software Engineer will primarily use technologies such as Python, Kubernetes, Docker, and major cloud service providers like AWS, Azure, or Google Cloud Platform. Familiarity with machine learning frameworks like TensorFlow, PyTorch, and data processing tools like Apache Kafka and Apache Airflow will be essential for successful project execution. Additionally, tools for version control (e.g., Git) and continuous integration/continuous deployment (CI/CD) practices will support workflow efficiency.

Ideal Candidates

Ideal candidates for this position are innovative thinkers with a strong passion for technology and data analytics. They possess excellent collaboration skills and thrive in a team-oriented environment. A self-driven approach to problem-solving and adaptability in embracing new challenges are crucial. Candidates who demonstrate curiosity about emerging technologies in AI/ML and seek to continuously improve their skills will find this role particularly rewarding. Furthermore, effective communication skills are important for interacting with both technical and non-technical stakeholders.