Master Frontier Engineer

About the role As a Master Frontier Engineer, you'll help define what's next in enterprise AI. Acting as the lead architect within a Frontier pod, you'll combine deep technical expertise, business acumen and strategic thinking to design AI-native systems that create measurable outcomes for some of the world's leading organizations. As a trusted technical advisor, you'll guide the architecture, governance and evolution of complex agentic systems, translate complex challenges into scalable AI solutions, establish engineering standards and mentor the next generation of Frontier talent, turning bold ideas into production-ready capabilities that create lasting value. In this role, you will Design and deploy advanced agentic systems and workflows, establishing governance models for permissions, context-sharing and data access. Architect enterprise-scale agent deployments on leading AI platforms, creating trust layers and cross-agent governance frameworks that enable responsible AI adoption. Define orchestration patterns, state management strategies, tool-calling frameworks and resilience models for complex AI ecosystems. Establish AI quality engineering practices, including agent evaluation frameworks, synthetic data strategies and agentic testing methodologies. Design and scale enterprise knowledge architectures, ontologies and retrieval systems that enable trustworthy, context-aware AI experiences. Work model We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days a week in a client or Cognizant office in Norway. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs. The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations. What you must have to be considered 7-15 years of delivery experience with deep AI-systems design expertise. Strong agentic system architecture skills, including AI orchestration and state-management patterns. Hands-on experience with enterprise agent platforms such as Copilot Studio or Agentforce, and MCP/A2A agent protocols. Expertise in RAG-at-scale and grounding strategy, plus knowledge graph and ontology engineering. Agentic test-framework design experience together with AI security and access controls. These will help you succeed AI-native solution architecture expertise paired with strong agentic engineering and orchestration. Context and retrieval engineering depth for grounding agents at enterprise scale. AI quality and evaluation design experience across agentic systems. Technical leadership and advisory skills, with strong client engagement and the ability to mentor engineers. Domain fluency, contextual judgment and a strong business-outcome orientation.