Senior AI Solution Designer

Purpose

As a Senior AI Solution Designer you will be placed in Group Data & AI and you will be responsible for the end-to-end AI architecture and standards to ensure robustness, reusability, scalability and cost efficiency of AI solutions.

What you will be doing

• Responsible for the design and continuous evolution of the Bank’s end-to-end AI architecture, including GenAI and Large Language Model (LLM) capabilities, ensuring alignment with the overall Enterprise IT Architecture. 
• Defines architecture standards, reusable frameworks and best practices for scalable and secure AI solution delivery across business domains. 
• Defines enterprise integration patterns for embedding AI capabilities into banking applications, digital channels and core business processes. 
• Designs agentic AI solutions, including multi-agent architectures, orchestration layers, tool integration and autonomous workflow patterns. 
• Designs AIOps / MLOps architecture and tooling, including model lifecycle management, repository versioning, CI/CD automation, deployment controls and production monitoring. 
• Drives optimization of AI platforms, pipelines and deployed solutions with focus on scalability, resilience, performance and cost efficiency. 
• Ensures AI solutions comply with internal governance, security, privacy and regulatory requirements in collaboration with Risk, Security and Data Governance functions. 
• Collaborates closely with, IT Business Areas, Data Governance and Quality, Cybersecurity and business stakeholders to accelerate AI adoption across the Bank. 
• Provides technical mentoring and architecture guidance to Data Science and Data Engineering teams. 
• Evaluates emerging AI technologies, market trends and vendor solutions to inform the Bank’s AI roadmap and future-state architecture.

What you need to have

• A Bachelor’s degree in Computer Science, Engineering or other related field of study
• A Masters' degree in a relevant field of study (preferred)
• At least five (5) years of professional experience in data engineering and data architecture, of which 2+ in AI projects
• Strong expertise in genAI and agentic AI
• Experience in knowledge retrieval and RAG architectures, incl. experience with vector databases, embeddings and semantic search
• Watsonx & Copilot Studio exposure (nice to have)
• Strong expertise in enterprise data architecture, data management and scalable AI solution design within complex technology environments.
• Hands-on experience with Advanced Analytics, Machine Learning and Natural Language Processing, with strong mandatory expertise in GenAI and Large Language Models.
• Solid understanding of agentic AI frameworks, orchestration patterns and AI integration approaches for enterprise-grade use cases.
• Experience designing and implementing RAG architectures, including vector databases, embeddings, semantic search and knowledge retrieval patterns.
• Knowledge of model lifecycle management and LLMOps practices, including evaluation, monitoring, guardrails, versioning and production readiness controls.
• Exposure to IBM Watsonx, Copilot Studio, Microsoft Graph extensibility or plugin development will be considered an asset.