LLM Engineers

Master-Works; Riyadh; On-site; SAR 150,000 - 200,000; Full time

Job summary

A leading AI company in the Riyadh Region is seeking a skilled LLM Engineer to enhance and scale their AI capabilities. The role involves developing and deploying advanced language model systems, optimizing performance, and ensuring compliance with security standards. Ideal candidates should have a strong background in AI, experience with LLM deployment in cloud environments, and proficiency in Python. This position offers an exciting opportunity to work on innovative AI solutions.

Qualifications

• Hands-on experience with LLM development and deployment.
• Strong understanding of Retrieval-Augmented Generation, embeddings, and vector databases.
• Proficiency with Python or similar languages for AI/ML pipeline development.

Responsibilities

• Build and optimize RAG pipelines for efficient document processing.
• Collaborate with developers to deliver secure, observable LLM endpoints.
• Integrate LLM capabilities with enterprise data sources.
• Monitor system performance and fine-tune models based on feedback.

Skills

LLM development and deployment

Retrieval-Augmented Generation

cloud environments (AWS, Azure, GCP)

Python

container orchestration (Docker, Kubernetes)

Education

Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field

Tools

observability tools

Job description

Job Overview:

Master Works is looking for a highly skilled LLM Engineer to join our AI Core Delivery team within the AI & Analytics CoE. In this role, you will develop, deploy, and optimize advanced language model capabilities, including Retrieval-Augmented Generation (RAG) pipelines and agentic AI systems. You will play a key role in scaling AI Core as the central enterprise AI platform by ensuring production-ready performance, robust observability, and seamless integration of LLMs with internal and external systems.

Key Responsibilities

• Model Development & Optimization
• Build and optimize RAG pipelines for efficient document processing, indexing, and retrieval.
• Implement agentic AI logic for tool integration, task automation, and external data access.
• Enhance query understanding, response ranking, and AI-generated output quality.
• Develop evaluation benchmarks to measure LLM performance, accuracy, and relevance.
• LLMOps & Deployment
• Collaborate with API developers and backend engineers to deliver secure, observable LLM endpoints.
• Deploy and manage LLMs in cloud-native or hybrid environments with support for scalability and multi-cloud readiness.
• Implement CI/CD workflows for model updates, rollback mechanisms, and performance tuning.
• Backend & Data Integration
• Integrate LLM capabilities with enterprise data sources and knowledge bases.
• Support multi-source retrieval and embedding strategies to improve AI responses.
• Design pipelines that ensure low latency, high throughput, and fault tolerance.
• Security & Compliance
• Apply enterprise-grade authentication, access management, and encryption standards.
• Ensure compliance with internal governance policies, data privacy, and audit requirements.
• Continuous Improvement
• Monitor system performance, debug issues, and fine-tune models based on real-world feedback.
• Stay updated on advancements in LLM architectures, open-source tools, and evaluation frameworks.

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
• Hands-on experience withLLM development and deployment(OpenAI, Anthropic, Hugging Face, LLaMA, etc.).
• Strong understanding ofRetrieval-Augmented Generation (RAG), embeddings, and vector databases.
• Experience withcloud environments (AWS, Azure, GCP)and infrastructure-as-code (IaC).
• Proficiency withPython or similar languagesfor AI/ML pipeline development.
• Familiarity withcontainer orchestration (Docker, Kubernetes)and CI/CD practices.
• Knowledge ofobservability toolsfor monitoring AI performance in production environments.

Preferred Skills

• Experience integratingagentic AI capabilitieswith external APIs and automation tools.
• Strong grasp ofprompt engineering, contextual memory systems, and LLM evaluation.
• Exposure toenterprise AI security and compliance standards.