AI Engineer - Agentic

Master Works; Riyadh; On-site; SAR 240,000 - 360,000; Full time

Job summary

Master Works is seeking an Agentic AI Engineer to design, build, and deploy agentic AI systems capable of planning, reasoning, and acting in real-world business contexts. The role focuses on both single-agent and multi-agent orchestration, including RAG, memory, and inter-agent communication.

You will collaborate with Generative AI, application engineers, and product teams to move prototypes into production, ensuring safety, observability, and reliable user experiences.

Qualifications

• Hands-on experience building agentic AI systems.
• 2+ years in agentic AI, LLM-powered apps, or Generative AI.
• 5+ years of overall software engineering experience preferred.
• Experience with microservices on cloud platforms.

Responsibilities

• Architect, build, and deploy agentic AI systems integrating agents with models and tools.
• Develop RAG, memory, and reasoning pipelines grounded in real data.
• Design single- and multi-agent workflows including tool selection and handoffs.
• Optimize performance balancing autonomy, latency, and cost.
• Collaborate with Generative AI, MLOps, and product teams to productionize solutions.
• Monitor safety, accuracy, observability, and user experience.

Skills

Agentic AI concepts

LLM-powered applications

Software engineering

Inter-agent communication

Education

Bachelor's or Master's in CS/AI/ML

Tools

Kubernetes

CI/CD pipelines

AWS

Azure

Google Cloud Platform (GCP)

Job description

Master Works is looking for an Agentic AI Engineer to join our growing team and design, build, and deploy agentic AI systems that can plan, reason, and act. The role covers both single-agent and multi-agent systems, as well as orchestration of workflows involving retrieval-augmented generation (RAG), context engineering, reasoning, tool calling, memory, and inter-agent communication. This role is ideal for a strong software engineer with hands-on AI/ML development experience and a passion for transforming generative AI models into reliable, goal-driven systems capable of solving complex, real-world business problems.

Key Responsibilities

• Architect, build, and deploy agentic AI systems that integrate AI agents with foundation models, enterprise systems, third-party tools, and APIs using established agentic frameworks or custom-built orchestrators.
• Build and maintain retrieval-augmented generation (RAG), memory, and reasoning pipelines that ground agent decisions in reliable, real-world data and enable persistent, contextual, and adaptive behavior.
• Design single-agent and multi-agent workflows, including planning, delegation, tool selection, handoffs, inter-agent communication, and human-in-the-loop controls.
• Optimize orchestration and reasoning performance while balancing autonomy, latency, cost, interpretability, reliability, and maintainability.
• Collaborate with Generative AI engineers, application engineers, MLOps engineers, and product teams to move agentic AI solutions from prototype to production.
• Monitor, evaluate, and benchmark agent performance, and help ensure AI systems are safe, accurate, trustworthy, observable, and capable of delivering a high-quality user experience.
• Document agent architectures, communication flows, guardrails, context-engineering strategies, memory design, tool interfaces, and orchestration logic to ensure clarity and reproducibility.
• Stay current with advances in agentic AI, multi-agent orchestration, RAG, model-context protocols, cognitive architectures, evaluation methods, and AI safety mechanisms.
• Collaborate with cross-functional teams to design and deliver complete AI solutions for customers.

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.
• 2+ years of hands-on experience developing agentic AI systems, LLM-powered applications, or advanced Generative AI solutions.
• Preferably 5+ years of overall professional software engineering experience
• Contributions to open-source agentic AI frameworks, orchestration platforms, evaluation tooling, or agent communication protocols.
• Experience building microservices and deploying AI applications on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
• Experience with AI safety, guardrails, observability, traceability, evaluation, and explainability for agentic AI systems.
• Experience deploying LLM or agentic workloads in production using containers, Kubernetes, CI/CD pipelines, and modern MLOps or LLMOps practices.

Job Location: HQ