Professional Experience15+ years in IT services or banking technology, with at least 5 years in AI/ML delivery or AI product leadership roles. Proven track record delivering production-grade AI solutions in large enterprise banking environments — not advisory or POC work only. Hands-on experience with LLM deployments — open-source models (QWEN, LLaMA, Mistral), prompt engineering, RAG pipelines, and fine-tuning. Experience operating embedded at GCC or ME banking institutions; familiarity with Saudi/UAE tier-1 banks preferred. Background in at least two of: AI-augmented testing, intelligent RPA, AI for banking operations, conversational AI in financial services.

Technical & AI Expertise LLM & AI Platforms: Open-source LLMs (QWEN, LLaMA 3, Mistral) and cloud LLM APIs (Azure Open AI, AWS Bedrock, Google Gemini, Anthropic Claude); vector databases (Pinecone, Weaviate, Chroma); RAG architecture. GPU Infrastructure: VRAM sizing, quantisation (GGUF, GPTQ), inference optimisation (v LLM, Ollama), and on-premise LLM serving. Cloud & Hybrid AI: Hybrid AI architecture design balancing data sovereignty and cloud scale; Azure AI Studio, AWS Sage Maker, or Google Vertex AI for MLOps. Banking AI: KYC/AML, credit risk, fraud detection, regulatory reporting automation, AI-augmented testing, and intelligent document processing. Agentic AI: Multi-agent AI frameworks, agentic workflow design, and enterprise AI platform evaluation
.
Leadership & Commercial Skills Executive presence — able to present AI strategy to C-suite and equally engage ML engineers and delivery teams. Strong commercial acumen: ROI modelling, AI-enabled service pricing, and productivity KPI tracking. Experience managing multi-vendor AI delivery environments and third-party tooling agreements. Bilingual proficiency in English and Arabic (Modern Standard Arabic and/or Gulf dialect) — essential for this engagemen
t.