Enterprise AI Architect (Principal / Expert)

For our client – an international FinTech & Telecom group – we are building a world-class AI capability (AI Squad) from the ground up. We create platforms, agents, and intelligent systems that will power the next generation of financial services serving millions of users and high-volume transactions.

• Minimum 8–10 years of experience in software, data, or machine learning engineering, including at least 3 years in an Architecture or Technical Design Authority role.
• Proven hands-on track record of designing and deploying AI/ML systems running at scale in production.
• Deep practical knowledge of large language models (LLM), Retrieval-Augmented Generation (RAG), embeddings, vector databases, and context engineering.
• Experience designing data and integration architecture across complex, heterogeneous enterprise systems.
• Exposure to regulated environments and understanding of security, data privacy, and model risk review frameworks.

Nice to have:

• Experience in FinTech, banking, or other high-volume transactional environments.
• Recognized cloud or architecture certifications (e.g., TOGAF, AWS/Azure/GCP Solutions Architect).
• Postgraduate qualification (Master's or Ph.D.) in a technical discipline (Computer Science, Data Science, Mathematics, or a related field).

AI Reference Architecture:

• Define the target enterprise AI reference architecture and solution patterns for the entire organization.

Standards & ADRs:

• Establish model selection standards (LLM), build Architectural Decision Records (ADRs), and define reusable solution patterns.

Data & Knowledge Architecture:

• Design data processing, knowledge retrieval (RAG), embedding estates, and vector/knowledge store architectures.

Interoperability & Integration:

• Ensure seamless integration and interoperability between AI components, core transaction platforms, data sources, and enterprise systems.

Architectural Assurance:

• Create reference implementations/prototypes, conduct code/design reviews, and maintain technical accountability for everything delivered to production.