AI Solution Architect

We are seeking an AI Solution Architect to translate complex banking business needs into scalable, secure enterprise AI system blueprints. This role owns end-to-end technical delivery from high-level design through production deployment while ensuring every solution aligns with the demands of a highly regulated financial IT landscape. Key Responsibilities • Design high-level and low-level architectures for banking AI applications • Securely integrate modern AI applications with core banking systems and databases • Evaluate and select optimal LLMs, vector stores, and machine learning platforms • Ensure enterprise-grade high availability, low latency, and system scalability • Embed financial regulatory compliance, risk mitigation, and data privacy into every design decision • Partner with engineering, security, and platform teams through the full delivery lifecycle 8+ years in enterprise IT, including 3+ years architecting AI solutions specifically within banking/financial services Hands-on experience with AI & data platforms: DataRobot, SAS Enterprise Guide, and open-source LLM frameworks Strong grounding in architecture patterns: microservices, API gateways, event-driven architecture, secure banking cloud landing zones CI/CD & MLOps proficiency: GitLab CI/CD, Docker, Kubernetes, MLflow, automated model testing frameworks Advanced Python skills Pandas, NumPy, Matplotlib, Seaborn, LangChain, LlamaIndex Strong communication skills to translate technical architecture into business-relevant terms for stakeholders Why Join Us • Architect enterprise-scale AI solutions inside a live banking transformation program • Own technical decisions from blueprint to production real build authority, not just advisory • Work at the intersection of cutting-edge AI and strict financial-sector compliance • Be part of a forward-looking organization driving AI-led transformation in the region 8+ years in enterprise IT, including 3+ years architecting AI solutions specifically within banking/financial services Hands-on experience with AI & data platforms: DataRobot, SAS Enterprise Guide, and open-source LLM frameworks Strong grounding in architecture patterns: microservices, API gateways, event-driven architecture, secure banking cloud landing zones CI/CD & MLOps proficiency: GitLab CI/CD, Docker, Kubernetes, MLflow, automated model testing frameworks Advanced Python skills Pandas, NumPy, Matplotlib, Seaborn, LangChain, LlamaIndex Strong communication skills to translate technical architecture into business-relevant terms for stakeholders