Job description
Job description
This is a chance to build production-grade Agentic AI systems for a Dubai-based technology business developing advanced AI products and intelligent automation solutions.
The focus here is not experimentation for the sake of it. They’re building secure, private and self-hosted AI environments that can be deployed into real business settings, with a strong emphasis on reliability, scalability and commercial use.
They’re now looking for a Senior Agentic AI Engineer who can take ownership across the full journey, from architecture and model deployment through to agent orchestration, application development and production operations.
The role
You’ll design and build AI agent applications using self-hosted LLMs and modern agentic frameworks, with a particular focus on privacy, security and control.
That means creating end-to-end agent workflows, integrating them into enterprise systems and internal applications, and building the backend services and tools needed to make those solutions usable in production.
You’ll also own the wider AI lifecycle, covering deployment, monitoring, versioning, performance optimisation and continuous improvement. The environment spans both cloud and on-premises infrastructure, with Docker, Kubernetes, CI/CD and MLOps forming a big part of how the systems are delivered.
There’s also a strong product element to the role. You’ll work closely with Product, Engineering and business stakeholders to turn complex requirements into commercial AI products that balance performance, scalability, cost and speed to market.
You’ll be expected to provide technical leadership and support other engineers, while staying hands-on with the technology yourself.
What we're looking for
5+ years' experience across AI or Machine Learning Engineering.
Strong hands-on experience building and deploying production-grade Generative AI and Agentic AI systems.
Experience deploying self-hosted LLMs in private or on-premises environments.
Strong Python skills and experience building scalable AI workflows, backend services and production applications.
Experience with LLMs such as LLaMA, Mistral, GPT or Claude, alongside frameworks such as LangChain, Semantic Kernel, CrewAI or MCP-based architectures.
Practical experience with RAG, prompt engineering, model evaluation, fine-tuning and optimisation.
Strong understanding of Docker, Kubernetes, CI/CD and MLOps.
Good knowledge of secure system design, including authentication, authorisation, encryption, data isolation and audit logging.
Experience with SQL / NoSQL databases, ETL / ELT pipelines and large-scale datasets.
Experience deploying AI systems across both cloud and on-premises environments.
AWS experience would be a plus, as would a Master’s degree in Computer Science, Data Science or a related technical field.