AI Architect

About the role

As an AI Architect at Prudential, you act as the bridge between business strategy and AI technology, helping business stakeholders identify, shape and prioritise AI opportunities that deliver measurable outcomes. You partner with business leaders, product owners and technology teams to turn business challenges into scalable AI, machine learning and generative AI solutions.

Key responsibilities

• Engage senior business leaders to uncover opportunities and advise stakeholders on AI strategy and adoption
• Build business cases and demonstrate value realisation
• Own solution envisioning, architecture design and adoption strategy
• Design end-to-end AI, ML and generative AI architectures that are scalable, secure and aligned with enterprise standards
• Define architecture patterns, reference designs and reusable AI capabilities for enterprise-wide adoption
• Lead solution design for emerging technologies such as Agentic AI, RAG architectures, MCP servers, model APIs and AI orchestration frameworks
• Define best practices for deployment, MLOps, LLMOps, monitoring and lifecycle management
• Drive standardisation of AI design patterns, architecture principles and development methodologies

Required profile

• 5-7+ years of experience in AI, data, solution architecture or enterprise architecture roles
• Proven experience leading AI discovery, solution design and architecture engagements with business stakeholders
• Experience delivering enterprise AI and generative AI solutions from concept through implementation
• Strong understanding of AI governance, security, compliance and responsible AI
• Experience in insurance, financial services or highly regulated industries is advantageous
• Master's or Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology or a related discipline

Required skills

• AI, machine learning, generative AI, LLMs, NLP and Agentic AI frameworks
• AI solution architectures with LangChain, LangGraph, RAG frameworks, MCP servers and AI orchestration platforms
• Cloud AI platforms on Azure and GCP
• MLOps, LLMOps, model governance, observability and responsible AI
• Data architecture, APIs and scalable enterprise platforms
• Reusable services and AI enablement capabilities are advantageous; Databricks, MLFlow and modern AI/ML ecosystems are a plus