Principal Platform Architect | Agentic AI

Our NielsenIQ Technology teams are building and evolving Connect, a unified, global, open data ecosystem powered by cloud and on-premises technologies. Clients around the world rely on NielsenIQ data and insights to innovate and grow. As a Principal Architect within the Enterprise Architecture team, you will define architecture for key components and capabilities across NIQ platforms, ensuring alignment with business strategy, product objectives, technology standards, and the long-term platform vision. You will partner with architecture peers, Product and Operations leaders, engineering leaders, and development teams to take solutions from initial requirements through design, delivery, and production operation. A key focus of this role is shaping the responsible adoption of Artificial Intelligence, Generative AI, and agentic AI across the enterprise. The successful candidate will combine broad enterprise architecture experience with practical knowledge of AI-enabled systems and will grow into an end-to-end platform architect as their knowledge of NIQ's platforms expands.
Responsibilities
• Lead the development of architecture designs for key products and capabilities across NIQ platforms, working closely with product stakeholders, engineering teams, and other architects.
• Define high-level solution approaches and blueprints that guide component-level design while maintaining an end-to-end view of platform integration, future products, and the technology roadmap.
• Create, maintain, and promote reference architectures, architecture principles, standards, patterns, and decision records for key areas of the platform.
• Shape enterprise architecture for AI, Generative AI, and agentic AI solutions, including model integration, retrieval-augmented generation, orchestration, tool and API use, memory, workflow automation, and human-in-the-loop controls.
• Define reusable patterns for secure and scalable AI agents, including identity and access control, data grounding, context management, guardrails, evaluation, observability, auditability, failure handling, and cost management.
• Partner with security, privacy, legal, data governance, and Responsible AI stakeholders to ensure AI solutions meet enterprise requirements for confidentiality, intellectual property, regulatory compliance, transparency, and risk management.
• Assess emerging technologies and viable alternatives for front-end applications, platform services, data pipelines, databases, AI platforms, foundation models, vector stores, and agentic frameworks, including build-versus-buy recommendations.
• Establish model and solution evaluation criteria covering quality, accuracy, groundedness, safety, latency, scalability, resilience, and total cost of ownership.
• Identify future-state solutions and drive innovation through proofs of concept, technology migrations, controlled pilots, and productionization.
• Provide architecture leadership, coaching, and design governance, promoting pragmatic, high-quality solutions and consistent engineering practices across teams.
Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
8+ years of experience in application development, solution design, or software architecture, supported by a strong engineering background and experience with enterprise-scale platforms.
Demonstrated experience designing and delivering scalable, secure, highly available, and observable distributed systems in production.
Strong knowledge of enterprise data architecture, data modelling, and high-volume processing across relational and non-relational data stores; experience with multi-terabyte databases is strongly preferred.
Experience with Azure AI services, Azure AI Foundry, Azure OpenAI, or comparable enterprise AI platforms.
Familiarity with agentic AI frameworks and interoperability standards, such as semantic orchestration frameworks, multi-agent frameworks, and the Model Context Protocol.
Experience defining Responsible AI governance, AI risk controls, threat modelling, red-teaming, or security patterns for AI-enabled applications.
Strong knowledge of data structures, algorithms, and design for performance, scalability, availability, resilience, privacy, and security.
Good knowledge of cloud architecture, preferably Microsoft Azure, including identity, networking, security, data, integration, monitoring, and platform services.
Practical experience architecting or delivering AI or Generative AI solutions in production, with an understanding of large language models, embeddings, vector search, retrieval-augmented generation, prompt and context engineering, and model evaluation.
Understanding of agentic AI architecture patterns, including agent orchestration, planning and reasoning workflows, tool calling, state and memory management, multi-agent coordination, human oversight, and safe execution boundaries.
Ability to communicate complex architecture decisions clearly to technical and non-technical stakeholders and to influence outcomes across organizational boundaries.

Employment type
Full-time

Work arrangement
Hybrid