AI Engineer

• Design and build RAG pipelines, including document ingestion, chunking, embeddings, hybrid search and re-ranking
• Build and maintain knowledge bases and knowledge graphs, including Neo4j or GraphRAG approaches
• Fine-tune LLMs using LoRA/QLoRA and PEFT on domain-specific data, and evaluate them against base models
• Develop agentic AI workflows with tool calling, multi-step orchestration and memory
• Set up LLM evaluation frameworks measuring accuracy, hallucination rate, latency and cost
• Deploy and operate models in production through LLMOps/MLOps, including monitoring, versioning and CI/CD
• Apply security, data privacy and responsible AI practices in regulated banking environments
• Work with architects, developers and client stakeholders to turn business needs into AI solutions
• Build production-grade LLM applications, including retrieval-augmented systems, knowledge graphs and fine-tuned models, for digital banking clients across the GCC

• 5–7 years in software, ML or AI engineering, with at least 2 years building LLM-based applications
• Strong Python; hands-on with LangChain, LlamaIndex or similar frameworks
• Proven experience building RAG systems in production
• Hands-on with vector databases (Pinecone, Weaviate, Milvus, pgvector, Azure AI Search or similar)
• Experience with knowledge graphs and graph databases
• Proven experience 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, Cloud experience on Azure, AWS or GCP, plus 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

• Remote work