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

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Overview

The Python and Kubernetes Software Engineer - Data, AI/ML & Analytics at Canonical will play a pivotal role in the development and enhancement of cutting-edge data analytics platforms. The successful candidate will be responsible for designing, building, and optimizing scalable applications in Python, specifically focusing on integrating machine learning capabilities with Kubernetes orchestration. This position will require collaboration with data scientists and engineers to ensure robust, secure, and efficient data pipelines, ultimately driving innovative solutions that leverage AI/ML for data analytics.

Job Responsibilities

• Design and implement scalable Python applications for data processing and analytics.
• Develop Kubernetes-based solutions to facilitate efficient deployment and orchestration of data-driven applications.
• Collaborate with cross-functional teams to define data requirements and establish analytical pipelines.
• Integrate machine learning models into production environments for real-time analytics.
• Participate in code reviews, ensuring code quality and adherence to best practices.
• Monitor and optimize the performance of data processing workflows and applications.
• Provide technical support and documentation for development processes and solutions.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• Proficiency in Python programming, with a focus on data handling libraries (e.g., Pandas, NumPy).
• Experience with Kubernetes for container orchestration and deployment.
• Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, Scikit-learn).
• Strong understanding of system design, architecture, and API development.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) is desirable.
• Effective communication skills and ability to work collaboratively in a team environment.

Technologies & Tools

The role will primarily utilize technologies such as Python, Kubernetes, Docker, and various machine learning frameworks. Familiarity with data visualization tools and specific database technologies such as PostgreSQL or MongoDB is also advantageous. Additionally, experience with CI/CD pipelines and version control systems like Git will be crucial for maintaining code quality and efficiency.

Ideal Candidates

Ideal candidates for this position will be analytical thinkers with a strong problem-solving mindset. They should possess a keen attention to detail and demonstrate a passion for innovation in data analytics and AI/ML technologies. The ability to work independently and collaboratively within teams is essential, along with a commitment to continuous learning and professional growth in the rapidly evolving tech landscape.