CoorB

AI Engineer

Posted 17 Days Ago

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Hiring Remotely in ARE

Remote

Senior level

Software

The Role

Design, build, fine-tune, evaluate, and deploy production-grade LLM solutions for digital banking. Responsibilities include RAG pipelines, knowledge graphs, agentic AI workflows, LLM evaluation, LLMOps/MLOps, model monitoring, CI/CD, security, privacy, and responsible AI. The role collaborates with architects, developers, and banking clients to deliver compliant AI solutions across the GCC.

Summary Generated by Built In

This is a remote position.

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 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.

Skills Required

5–7 years of experience in software, ML, or AI engineering, including at least 2 years building LLM-based applications
Strong Python skills
Hands-on experience with LangChain, LlamaIndex, or similar frameworks
Proven experience building production RAG systems
Hands-on experience with vector databases such as Pinecone, Weaviate, Milvus, pgvector, or Azure AI Search
Experience with knowledge graphs and graph databases
Proven experience fine-tuning open-source LLMs such as Llama, Mistral, or Qwen using Hugging Face tooling
Experience with commercial LLM APIs including OpenAI, Azure OpenAI, Anthropic, or Google Gemini
Experience building agentic AI and multi-agent solutions, including MCP
On-premises or cloud experience with Azure, AWS, or GCP
Experience with Docker and Kubernetes
Solid understanding 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