Senior Manager - AI Solution Architect

Senior Manager – AI Solution Architect Location: Abu Dhabi, United Arab Emirates Industry: Financial Services Employment Type: Full-time, Permanent Role Overview A tier-one financial institution in UAE is seeking a Senior Manager – AI Solution Architect to lead the design, architecture, and integration of cutting-edge Artificial Intelligence platforms across digital value streams. Operating within a scaled agile model, you will translate enterprise product vision into scalable, secure, and resilient AI architectures. Your focus will center on integrating Generative AI, Large Language Models (LLMs), agentic systems, RAG pipelines, and Machine Learning (ML) platforms into intelligent customer and employee journeys. Key Responsibilities • AI Architecture & Solution Design: Architect end-to-end AI solutions, including LLM-based agents, Retrieval-Augmented Generation (RAG) pipelines, conversational AI, and vector databases. Evaluate and select frameworks (e.g., LangChain, Azure OpenAI, Pinecone). • Agile Engineering Enablement: Coach and guide technical leads and development squads on modern AI patterns, architecture, and engineering principles to elevate platform maturity and reduce technical debt. • Platform Governance & Security: Maintain platform architecture documentation, ensuring all deployed AI models adhere to enterprise security, data privacy, compliance, and Responsible AI principles. • Stakeholder & Business Alignment: Act as the primary technical interface between business leaders, product owners, and engineering teams to articulate the business value of AI capabilities and translate requirements into delivery backlogs. • Innovation & Continuous Improvement: Stay at the forefront of emerging AI technologies (multimodal AI, agentic workflows, RLHF) and run rapid prototypes/proofs-of-concept for enterprise applicability. Candidate Requirements Experience & Qualifications • Minimum 7 years of experience within Banking or Financial Services (preferably within Enterprise/Technology Architecture). • Minimum 3 years dedicated focus on AI/ML solution design, deployment, and Generative AI platforms. • Education: Bachelor’s degree in Computer Science, Artificial Intelligence, or Software Engineering (Master’s preferred). • Certifications: Cloud Solutions Architect (Azure or AWS); specialized AI/ML certifications (e.g., Azure AI Engineer) are advantageous. Technical & Core Skills • Deep expertise in LLMs, RAG architectures, prompt engineering, embeddings, and vector databases (e.g., Pinecone, FAISS). • Hands-on experience with cloud platform deployments (Azure, AWS) and serverless AI frameworks. • Strong background in Scaled Agile (SAFe) methodologies, API reusability, and microservice architectures. • Proven ability to establish Responsible AI governance frameworks and regulatory compliance standards. • Superior communication skills with a track record of driving cross-functional alignment. If you meet the above criteria and would like to explore this opportunity, please apply directly through this posting and attach your CV. Show more Show less

Minimum 7 years of experience within Banking or Financial Services (preferably within Enterprise/Technology Architecture). Minimum 3 years dedicated focus on AI/ML solution design, deployment, and Generative AI platforms. Education: Bachelor’s degree in Computer Science, Artificial Intelligence, or Software Engineering (Master’s preferred). Certifications: Cloud Solutions Architect (Azure or AWS); specialized AI/ML certifications (e.g., Azure AI Engineer) are advantageous.

AI Architecture & Solution Design: Architect end-to-end AI solutions, including LLM-based agents, Retrieval-Augmented Generation (RAG) pipelines, conversational AI, and vector databases. Evaluate and select frameworks (e.g., LangChain, Azure OpenAI, Pinecone). Agile Engineering Enablement: Coach and guide technical leads and development squads on modern AI patterns, architecture, and engineering principles to elevate platform maturity and reduce technical debt. Platform Governance & Security: Maintain platform architecture documentation, ensuring all deployed AI models adhere to enterprise security, data privacy, compliance, and Responsible AI principles. Stakeholder & Business Alignment: Act as the primary technical interface between business leaders, product owners, and engineering teams to articulate the business value of AI capabilities and translate requirements into delivery backlogs. Innovation & Continuous Improvement: Stay at the forefront of emerging AI technologies (multimodal AI, agentic workflows, RLHF) and run rapid prototypes/proofs-of-concept for enterprise applicability.