About the Role
We are seeking a Senior AI Engineer to design, build, and deploy AI solutions specifically for digital banking. In this role, you will develop production-grade LLM applications such as retrieval-augmented systems, knowledge graphs, and fine-tuned models that deliver measurable business value to our banking clients across the GCC.
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Key Responsibilities
- Design and build RAG pipelines: document ingestion, chunking, embeddings, hybrid search, and re-ranking.
- Build and maintain knowledge bases and knowledge graphs (for example Neo4j, or GraphRAG approaches).
- Fine-tune LLMs (LoRA/QLoRA, PEFT) on domain-specific data and evaluate them against base models.
- Develop agentic AI workflows, including tool calling, multi-step orchestration, and memory.
- Set up LLM evaluation frameworks to measure accuracy, hallucination rate, latency, and cost.
- Deploy and operate models in production (LLMOps/MLOps), including monitoring, versioning, and CI/CD.
- Apply security, data privacy, and responsible AI practices suited to regulated banking environments.
- Collaborate with architects, developers, and client stakeholders to turn business needs into effective AI solutions.
Requirements
Mandatory Requirements
All of the following are required:
- 5–7 years in software, ML, or AI engineering, with at least 2 years building LLM-based applications.
- Strong Python; hands-on experience with LangChain, LlamaIndex or similar frameworks.
- Proven experience building RAG systems in production.
- Hands-on experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, Azure AI Search or similar).
- Experience with knowledge graphs and graph databases.
- Proven experience in fine-tuning open-source LLMs (Llama, Mistral, Qwen or similar) using Hugging Face tooling.
- Experience with commercial LLM APIs (OpenAI/Azure OpenAI, Anthropic, Google Gemini).
- Experience building agentic AI and multi-agent solutions, including MCP (Model Context Protocol).
- On-premises and Cloud experience on Azure, AWS or GCP, as well as Docker and Kubernetes.
- Solid grasp of prompt engineering, embeddings, and LLM evaluation methods.
- Arabic NLP or multilingual model experience.
- Experience in banking, fintech or other regulated industries.
- Familiarity with SAMA or other GCC regulatory and data-residency requirements.
