Senior Manager AI Engineering (GenAI & MLOps Platform)

Client of Discovered MENA

As Senior AI Engineering Manager, you will lead the development of the organisation s GenAI and MLOps platform, ensuring that AI solutions can be built, deployed, and operated at scale.

This role requires a leader who is highly technical and hands-on, capable of guiding architecture decisions while also contributing directly to engineering and platform development.

Discover the Responsibilities:

• Lead the design and development of the organisation s Generative AI and MLOps platform, enabling scalable AI deployment across the business.
• Build and manage modern AI infrastructure, including Kubernetes environments, CI/CD pipelines, and infrastructure-as-code frameworks.
• Develop and maintain platform capabilities for GenAI applications, including RAG architectures, model deployment, monitoring, and governance.
• Establish engineering best practices, tooling, and platform standards to support AI engineers building and deploying AI solutions.
• Collaborate with data scientists, AI engineers, and product teams to support the delivery of conversational AI, chatbots, and agentic AI applications.
• Lead and mentor a team of engineers and contractors, ensuring strong technical direction and delivery quality across AI platform initiatives.
• Contribute to the scaling and evolution of enterprise AI platform capabilities as part of the wider AI transformation programme.

Discover the Requirements:

• 10+ years of experience in AI engineering, machine learning engineering, or data platform engineering roles.
• Strong hands-on experience building MLOps platforms and AI infrastructure using technologies such as Kubernetes, Docker, and CI/CD frameworks.
• Experience building and deploying Generative AI applications, including RAG architectures, conversational AI, or agentic workflows.
• Strong cloud experience with AWS or Azure, including infrastructure-as-code and scalable ML deployment environments.
• Proven experience leading technical teams while remaining hands-on with architecture and code.
• Strong understanding of ML lifecycle management, model deployment, monitoring, and governance.
• Excellent stakeholder management skills and the ability to operate in fast-paced, high-impact AI environments.