# AI Architect, Enterprise Agentic AI Architecture — TEN-XER

Canonical: https://jobxdubai.com/jobs/li-4472338656-ai-architect-enterprise-agentic-ai-architecture
Location: Dubai, UAE
Type: full_time · Level: lead
Monthly salary: AED 35,000 to 60,000 per month (estimated, not employer-stated) (UAE salaries are tax-free)
Posted: 2026-09-30
Apply: https://www.linkedin.com/jobs/view/ai-architect-enterprise-agentic-ai-architecture-at-ten-xer-4472338656?_l=en

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## Description

Job Purpose
We are looking for an experienced AI Architect responsible for architecting enterprise-wide Generative AI and Agentic AI
capabilities across banking systems. The role will define target architecture, integration patterns, standards, reference
implementations and reusable building blocks for full-fledged AI chat assistants, autonomous agents and multi-agent
workflows.
The AI Architect will lead end-to-end architecture for agentic retail banking journeys such as payments, transfers,
servicing, self-service fulfilment and customer assistance, ensuring secure integration with enterprise APIs, middleware,
core banking platforms and customer-facing channels across web and mobile.
This role requires deep hands-on AI engineering capability, strong banking domain understanding, practical delivery
experience with multiple production-grade banking agents, and the ability to collaborate with product, engineering,
infrastructure, cybersecurity, data, governance and enterprise architecture teams to present and align designs through
ARB.
Key Accountabilities
• Define and own enterprise-wide Agentic AI architecture, reference patterns, reusable components and governance
guardrails for banking use cases.
• Architect full-fledged conversational AI and agent platforms for retail banking, including customer assistance,
servicing, payments, transfers and operational self-service journeys.
• Translate business, product and regulatory requirements into scalable, secure, observable and resilient AI solution
architectures.
• Design multi-agent ecosystems with planning, orchestration, tool usage, action execution, memory, context
management, evaluation loops and human-in-the-loop controls.
• Lead the architecture of RAG platforms, knowledge retrieval, semantic indexing, vector databases, grounding
strategies and answer relevancy improvement mechanisms.
• Define token-efficient context engineering patterns to maximize output quality while reducing token utilization,
inference latency and run cost.
• Set architecture standards for model evaluation, agent evaluation, guardrails, auditability, observability,
performance, safety and business outcome measurement.• Architect secure integration with enterprise APIs, backend systems, middleware, channels, data platforms, AI services
and third-party tools using approved enterprise patterns.
• Create Architecture Decision Records, solution architecture documents, integration designs and ARB-ready
presentations to obtain required architecture approvals.
• Collaborate across enterprise architecture, cybersecurity, infrastructure, engineering, product, operations and
business teams to build a sustainable AI ecosystem.
Job Context
Specific Accountability
• Architect and guide implementation of multiple production-grade banking agents, including full-fledged chat
assistants with tool execution, transaction flows and contextual customer support.
• Design agentic workflows for payments, transfers, account/card servicing and self-service fulfilment with strong
controls for authorization, exception handling, recovery and audit trails.
• Design and implement human-in-the-loop patterns for approvals, exception handling, risk-based escalation,
operational review and sensitive customer journeys.
• Define memory architecture including short-term memory, user/session context, long-term knowledge,
personalization boundaries, privacy constraints and secure persistence patterns.
• Architect context management and prompt/context engineering patterns that improve accuracy, reduce latency and
optimize token usage across complex multi-step tasks.
• Design evaluation frameworks for agents, RAG and workflows covering task completion, grounding, faithfulness,
retrieval quality, safety, latency, cost, tool success rate and customer experience.
• Architect enterprise RAG systems including ingestion, redaction, chunking, embeddings, vector search, ranking,
reranking, citation/grounding and quality feedback loops.
• Design and govern tool integration patterns, tool registries, action APIs, orchestration layers and MCP servers for
agent-to-tool and agent-to-system connectivity.
• Apply and guide implementation of agent and AI protocols including MCP, A2A, A2UI and other relevant
interoperability protocols for multi-agent and user-interface integration patterns.
• Define security architecture for AI systems aligned to banking security requirements, zero trust principles, least
privilege, identity-based access, data protection and secure API integration.
• Architect PII redaction, data masking, privacy controls, prompt/input/output filtering, secure logging and audit
mechanisms for regulated banking workloads.
• Design deployment architecture using Azure AI services, Azure OpenAI, AWS AI services, Amazon Bedrock,
Kubernetes, serverless components, observability tools and resilient infrastructure patterns.
• Recommend infrastructure, network, compute, storage, vector database, observability and platform capabilities
required to build and scale enterprise agent architecture.
• Provide hands-on technical direction when required, including building complex proof-of-concepts, reference
implementations, agent workflows and reusable engineering accelerators.
• Mentor AI engineers and architects on agentic architecture patterns, responsible AI, secure design, architecture
trade-offs and engineering best practices.
Added Advantage
environments.
• Prior experience building AI agent ecosystems in regulated financial services or large-scale digital banking
• Hands-on experience with Microsoft Semantic Kernel, AutoGen, LangChain, LangGraph and Microsoft Agent
Framework or equivalent Microsoft-native agent development capabilities.
• Experience establishing platform standards, reusable architecture blueprints and governance models for enterprise
AI adoption.
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## Requirements

Minimum Qualification • Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science or a related discipline. • Relevant certifications in cloud architecture, AI engineering, security architecture, enterprise architecture or machine learning are preferred. Minimum Experience • Senior technology professional with around 12+ years of experience across software engineering, architecture, cloud/platform engineering and enterprise solution delivery. • Minimum 4+ years of hands-on AI engineering or AI architecture experience, including Generative AI, LLM applications and Agentic AI solutions. • Proven experience architecting and delivering multiple banking agents or full-fledged banking chat assistant capabilities integrated with enterprise systems. • Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, customer self-service, operational controls and regulatory/security considerations.

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