AI Engineer

CoorB; Makkah Region; On-site; SAR 240,000 - 420,000; Full time

Job summary

CoorB is seeking a Senior AI Engineer to design, build and deploy AI solutions for digital banking in the GCC. You will deliver production-grade LLM applications, including retrieval-augmented systems, knowledge graphs and fine-tuned models that drive measurable business value for banking clients.

Join a team building RAG pipelines, managing vector databases, and deploying LLMs in production with robust security and governance for regulated environments.

Qualifications

• 5–7 years in software, ML or AI engineering with at least 2 years building LLM-based applications.
• Strong Python and 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 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.
• 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 GCC regulatory and data-residency requirements.

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.
• Work with architects, developers and client stakeholders to turn business needs into AI solutions.

Skills

Python

LLM engineering

RAG systems

Knowledge graphs

Multilingual NLP

Prompt engineering

Banking domain knowledge

Security & privacy

Tools

LangChain

LlamaIndex

Hugging Face tooling

Pinecone

Weaviate

Milvus

pgvector

Azure OpenAI

OpenAI API

Docker

Kubernetes

Job description

CoorB is looking for a Senior AI Engineer to design, build and deploy AI solutions for digital banking. You will build production-grade LLM applications, including retrieval-augmented systems, knowledge graphs and fine-tuned models, that deliver measurable business value to our banking clients across the GCC.

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.
• Work with architects, developers and client stakeholders to turn business needs into AI solutions.

Requirements

Mandatory Requirements

All of the following are required:

• 5–7 years in software, ML or AI engineer ing, 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 engineer ing, 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.