Senior Agentic AI Engineer

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.

• 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.

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