Senior Agentic AI Engineer

DISCOVERED; Dubai; On-site; AED 240,000 - 360,000; Full time

Job summary

DISCOVERED in Dubai is seeking a Senior Agentic AI Engineer to own the end-to-end lifecycle of production AI solutions. You will design and deploy agent workflows using self-hosted LLMs, integrate with enterprise systems, and build backend services for production reliability.

You'll lead architecture decisions, optimize performance, and collaborate with Product and Engineering to deliver scalable AI products in both cloud and on-prem environments, using Docker, Kubernetes, CI/CD, and MLOps

Qualifications

• 5+ years' experience across AI or Machine Learning Engineering.
• Strong hands-on experience building and deploying production-grade Generative AI and Agentic AI systems.

Responsibilities

• Design and build AI agent applications using self-hosted LLMs and modern agentic frameworks, focusing on privacy, security and control.
• Own the wider AI lifecycle including deployment, monitoring, versioning, and optimization.
• Collaborate with Product, Engineering and business stakeholders to deliver commercial AI products that balance performance, cost and speed to market.
• Provide technical leadership and hands-on contribution across the stack, from architecture to production operations.

Skills

AI/ML engineering

Production AI systems

Self-hosted LLMs

Python

LangChain / frameworks

RAG & prompt engineering

Docker & Kubernetes

CI/CD & MLOps

Secure system design

Cloud & on-prem deployment

Education

Master's degree in CS/Data Science or related field

Tools

LLaMA

Mistral

GPT/Claude

LangChain

Semantic Kernel

CrewAI

MCP-based architectures

Docker

Kubernetes

CI/CD

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.