Principal Lead, AI Platform & Solutions

Principal Lead, AI Platform & Solutions — First Abu Dhabi Bank Location: Abu Dhabi, UAE JOB SUMMARY / INTRODUCTION To lead the strategic design, architecture, and enterprise build-out of the bank's AI & Agentic Platform capability, ensuring it serves as a trusted, scalable, and governed foundation for AI-driven decisioning, agentic automation, and enterprise knowledge across the Group. The role is pivotal in shaping how AI capability is built, integrated, and adopted across business units, partnering with senior business and technology stakeholders to translate the bank's AI ambition into a coherent platform and solution architecture aligned with FAB's digital, agentic, and AI roadmap, regulatory expectations, and Group strategy. The role combines deep technical authority on AI platform architecture with the strategic judgement and stakeholder reach required to direct enterprise AI capability across a tier-one bank. KEY RESPONSIBILITIES Define and own the target architecture for the AI & Agentic Platform, covering the agentic runtime, action gateway, control plane, knowledge foundation, model gateway and abstraction, evaluation, and observability layers. Set and evolve the cloud architecture for AI workloads across Azure and AWS, including hybrid model serving, inference economics, network and identity design, data residency, and the build-versus-buy positioning across managed services and bank-built capabilities. Lead the integration architecture between the AI Platform and the bank's core estate, including core banking, risk and credit systems, treasury and finance platforms, the enterprise data platform (Azure and Cloudera), enterprise APIs, ESB, and identity services. Establish and govern the engineering, architectural, and AI engineering standards adopted across the AI Platform team, including reference patterns, agent and tool contracts, runtime governance hooks, evaluation discipline, and integration with model risk. Shape FAB's multi-year AI capability direction, including build-versus-buy positioning, foundation model strategy, proprietary capability investment, and platform evolution in response to changes in the agentic AI and regulatory landscape. Partner with the Senior, Platform Engineering and Architecture lead in a two-in-a-box operating model, with a clear separation between platform direction, target architecture, and enterprise positioning (this role) and platform engineering execution and run-state. Build enterprise AI capability across the Group through reference architectures, standards, communities of practice, internal forums, and direct mentorship of senior engineers and architects across DAAI and the wider technology function. Engage business heads and their CIOs across Wholesale Banking, Personal Banking, Private Banking, Treasury, Risk, and Group Finance to shape how AI is integrated into their operating models, beyond individual use cases. Represent the AI & Agentic Platforms function in forums with Group Technology, Enterprise Architecture, Cyber, Model Risk, Internal Audit, CBUAE, and external regulators on matters of AI architecture, governance, and trusted agentic operations. Manage strategic technology partnerships with hyperscalers, foundation model providers, agentic framework vendors, and SI partners on terms that protect FAB's intellectual property, capability, and optionality. Represent FAB externally in industry forums, regulator working groups, and peer banks, contributing to the Group's thought leadership on enterprise agentic AI. REQUIREMENTS / QUALIFICATIONS Technical Expertise Deep, current architectural knowledge of agentic AI systems, including the layered protocol stack (MCP for tool access, A2A for inter-agent coordination, ACP and emerging agentic standards), orchestration frameworks (LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, or equivalent), planning and reasoning patterns, multi-agent systems, evaluation, observability, and runtime governance. Strong working knowledge of foundation model architecture and integration patterns, including managed model APIs (Azure AI Foundry, AWS Bedrock, Google Vertex AI), open-weight model serving, model gateway design, prompt and context management, RAG, fine-tuning, and the cost-quality-latency trade-offs across model selection. Cloud architecture fluency on Azure and AWS at enterprise scale, covering networking, identity, data, model serving, inference cost management, and hybrid deployment patterns. Proven expertise in integrating AI and data platforms into the core estate of a bank or comparably regulated enterprise, including core banking systems, risk and credit platforms, enterprise data platforms (Azure, Databricks, Cloudera), APIs, ESB, and identity layers. Strong grounding in software and platform engineering, with ongoing hands-on credibility in architecture reviews, prototyping, and critical design decisions. Working knowledge of AI governance, mod

Technical Expertise Deep, current architectural knowledge of agentic AI systems, including the layered protocol stack (MCP for tool access, A2A for inter-agent coordination, ACP and emerging agentic standards), orchestration frameworks (LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, or equivalent), planning and reasoning patterns, multi-agent systems, evaluation, observability, and runtime governance. Strong working knowledge of foundation model architecture and integration patterns, including managed model APIs (Azure AI Foundry, AWS Bedrock, Google Vertex AI), open-weight model serving, model gateway design, prompt and context management, RAG, fine-tuning, and the cost-quality-latency trade-offs across model selection. Cloud architecture fluency on Azure and AWS at enterprise scale, covering networking, identity, data, model serving, inference cost management, and hybrid deployment patterns. Proven expertise in integrating AI and data platforms into the core estate of a bank or comparably regulated enterprise, including core banking systems, risk and credit platforms, enterprise data platforms (Azure, Databricks, Cloudera), APIs, ESB, and identity layers. Strong grounding in software and platform engineering, with ongoing hands-on credibility in architecture reviews, prototyping, and critical design decisions. Working knowledge of AI governance, mod

JOB SUMMARY / INTRODUCTION To lead the strategic design, architecture, and enterprise build-out of the bank's AI & Agentic Platform capability, ensuring it serves as a trusted, scalable, and governed foundation for AI-driven decisioning, agentic automation, and enterprise knowledge across the Group. The role is pivotal in shaping how AI capability is built, integrated, and adopted across business units, partnering with senior business and technology stakeholders to translate the bank's AI ambition into a coherent platform and solution architecture aligned with FAB's digital, agentic, and AI roadmap, regulatory expectations, and Group strategy. The role combines deep technical authority on AI platform architecture with the strategic judgement and stakeholder reach required to direct enterprise AI capability across a tier-one bank. KEY RESPONSIBILITIES Define and own the target architecture for the AI & Agentic Platform, covering the agentic runtime, action gateway, control plane, knowledge foundation, model gateway and abstraction, evaluation, and observability layers. Set and evolve the cloud architecture for AI workloads across Azure and AWS, including hybrid model serving, inference economics, network and identity design, data residency, and the build-versus-buy positioning across managed services and bank-built capabilities. Lead the integration architecture between the AI Platform and the bank's core estate, including core banking, risk and credit systems, treasury and finance platforms, the enterprise data platform (Azure and Cloudera), enterprise APIs, ESB, and identity services. Establish and govern the engineering, architectural, and AI engineering standards adopted across the AI Platform team, including reference patterns, agent and tool contracts, runtime governance hooks, evaluation discipline, and integration with model risk. Shape FAB's multi-year AI capability direction, including build-versus-buy positioning, foundation model strategy, proprietary capability investment, and platform evolution in response to changes in the agentic AI and regulatory landscape. Partner with the Senior, Platform Engineering and Architecture lead in a two-in-a-box operating model, with a clear separation between platform direction, target architecture, and enterprise positioning (this role) and platform engineering execution and run-state. Build enterprise AI capability across the Group through reference architectures, standards, communities of practice, internal forums, and direct mentorship of senior engineers and architects across DAAI and the wider technology function. Engage business heads and their CIOs across Wholesale Banking, Personal Banking, Private Banking, Treasury, Risk, and Group Finance to shape how AI is integrated into their operating models, beyond individual use cases. Represent the AI & Agentic Platforms function in forums with Group Technology, Enterprise Architecture, Cyber, Model Risk, Internal Audit, CBUAE, and external regulators on matters of AI architecture, governance, and trusted agentic operations. Manage strategic technology partnerships with hyperscalers, foundation model providers, agentic framework vendors, and SI partners on terms that protect FAB's intellectual property, capability, and optionality. Represent FAB externally in industry forums, regulator working groups, and peer banks, contributing to the Group's thought leadership on enterprise agentic AI. REQUIREMENTS / QUALIFICATIONS Technical Expertise Deep, current architectural knowledge of agentic AI systems, including the layered protocol stack (MCP for tool access, A2A for inter-agent coordination, ACP and emerging agentic standards), orchestration frameworks (LangGraph, Google ADK, LlamaIndex, Autogen, CrewAI, or equivalent), planning and reasoning patterns, multi-agent systems, evaluation, observability, and runtime governance. Strong working knowledge of foundation model architecture and integration patterns, including managed model APIs (Azure AI Foundry, AWS Bedrock, Google Vertex AI), open-weight model serving, model gateway design, prompt and context management, RAG, fine-tuning, and the cost-quality-latency trade-offs across model selection. Cloud architecture fluency on Azure and AWS at enterprise scale, covering networking, identity, data, model serving, inference cost management, and hybrid deployment patterns. Proven expertise in integrating AI and data platforms into the core estate of a bank or comparably regulated enterprise, including core banking systems, risk and credit platforms, enterprise data platforms (Azure, Databricks, Cloudera), APIs, ESB, and identity layers. Strong grounding in software and platform engineering, with ongoing hands-on credibility in architecture reviews, prototyping, and critical design decisions. Working knowledge of AI governance, model risk management, and regulatory expectations applicable to AI in financial services, in