Kubecost Product Intern

Introduction

Kubecost Product play a pivotal role in shaping offerings that leverage artificial intelligence, machine learning, and data analytics to help organizations understand, allocate, and optimize cloud and Kubernetes costs while solving complex business challenges. IBM follows a structured product lifecycle management process, integrating agile methodologies, data-informed decision-making, and cross-functional collaboration. Kubecost Product work across engineering, design, data science, and go-to-market teams to deliver innovative, secure, and scalable AI-powered solutions.

• Own problem discovery and solution definition for Kubernetes cost allocation, optimization, and cloud cost visibility within enterprise environments.
• Work with engineering teams to translate Kubernetes telemetry, cloud billing data (e.g., AWS/Azure/GCP), and cost models into actionable product features and dashboards.
• Analyze usage data and customer feedback to improve container cost monitoring, resource efficiency, and Fin Ops workflows.
• Collaborate with cross-functional teams including data scientists, engineers, designers, and GTM stakeholders to deliver high-quality features and models.
• Translate complex technical capabilities (e.g., ML models, data pipelines, APIs) into clear product requirements and user stories.
• Prioritize product backlog using data-driven frameworks and ensure alignment with roadmap and KPIs.
• Facilitate ethical AI practices by integrating fairness, transparency, and compliance into product development.
• Monitor product performance using analytics tools and user feedback to iterate and improve continuously.
• Communicate product vision, strategy, and progress to internal and external stakeholders, including executives and customers.
• Champion user experience and usability in AI interfaces, ensuring intuitive and trustworthy interactions.

Bachelor's Degree

• Strong product management fundamentals: roadmap planning, backlog grooming, stakeholder alignment, and go-to-market execution.
• Understanding of cloud computing fundamentals and containerized environments (Kubernetes).
• Familiarity with data concepts such as telemetry, metrics, and usage analytics.
• Understanding of AI/ML concepts, data lifecycle, and model deployment practices.
• Experience with Agile methodologies, including sprint planning, retrospectives, and iterative delivery.
• Proficiency in product analytics tools and data visualization platforms.
• Strong communication and storytelling skills to translate technical insights into business value.
• Ability to manage dependencies across teams and anticipate risks in product delivery.

• Exposure to cloud cost management, Fin Ops, or infrastructure optimization domains.
• Experience with enterprise AI products or platforms.
• Exposure to AI ethics frameworks and responsible AI practices.
• Comfortable working with global teams across time zones and cultures.