AI Platform Engineer

You will work closely with and report under Architecture in Data & AI. Much like our broader engineering teams within Schibsted Media Technology , you will be a key contributor in providing tooling that our teams use and love. You will collaborate with stakeholders and end-users across all of our organisation, including VG, Aftenposten, Aftonbladet, SvD, Omni and TV4, to ensure our AI solutions meet their expectations.

What will you do in this role?

As the System Owner for AI Tools and Platforms, you will take end-to-end responsibility for our enterprise AI portfolio (such as OpenAI/ChatGPT, Anthropic/Claude, and similar technologies). You will bridge the gap between technical infrastructure, security, and end-user needs. Relying heavily on your software engineering background, you will deeply understand, interpret, and securely execute complex AI use-cases across the organization.

Your Key Responsibilities Will Include

• System Ownership & Configuration: Acting as the primary system owner for enterprise AI platforms, managing technical configurations, integrations, and ongoing platform health.
• User Dialogue & Problem Solving: Engaging directly with end-users to understand their pain points, interpret incoming use-cases, and figure out how to best solve them using our available AI tools.
• Testing & Prototyping: Taking a hands-on approach to testing new models, prototyping workflows, and validating technical solutions before rolling them out to broader teams.
• Security Alignment: Partnering closely with the security team to ensure that all AI tools are configured and utilized in a highly secure manner, protecting sensitive company and customer data.
• Policy Development: Helping to write, implement, and iterate on internal policies and guidelines for safe, ethical, and effective AI usage.
• Cost Control: Monitoring platform usage, tracking API expenditures, and implementing cost-control measures to optimize our AI spending.

• Software Engineering Background: Foundation in software engineering, which is essential for understanding architectural security implications and translating user needs into technical solutions.
• Collaborative & Service-Minded: A pragmatic, service-minded approach with a strong focus on delivering real value to the business.
• Security & Policy Expertise: Solid understanding of system security principles, data privacy, and governance.
• Communication Skills: Strong interpersonal skills, with the ability to collaborate effectively with cross-functional teams, security experts, and non-technical stakeholders.
• Analytical Problem Solving: Excellent analytical skills to identify optimization opportunities, manage costs effectively, and drive results.

• Hands-on experience working directly with LLM APIs (e.g., OpenAI, Anthropic), prompt engineering, or AI orchestration frameworks.
• Understanding of cloud infrastructure (e.g., AWS) to help integrate AI tools smoothly with existing internal systems.