Engineering Manager
Role Overview
A hands-on technical leadership role leading the design, build, and deployment of production-grade AI platforms and solutions.
The role combines AI engineering depth, engineer leadership, roadmap development, and delivery of secure, scalable systems in a complex enterprise environment.
The position is with a major Abu Dhabi government entity and supports a government transformation programme.
Key Skills for This Role
Full Job Posting
Role overview
A hands-on technical leadership role leading the design, build, and deployment of production-grade AI platforms and solutions.
The role combines AI engineering depth, engineer leadership, roadmap development, and delivery of secure, scalable systems in a complex enterprise environment.
The position is with a major Abu Dhabi government entity and supports a government transformation programme.
Candidate requirements
- Strong background in AI engineering, machine learning engineering, software engineering, computer science, electrical engineering, or a related technical discipline.
- Proven experience building and deploying production AI, ML, or GenAI systems, not only proofs of concept or strategy documents.
- Ability to lead or mentor engineers, manage technical delivery, set engineering standards, and review implementation quality.
- Experience in enterprise, government, banking, defence, sovereign AI, or another regulated environment is highly relevant.
- Strong understanding of responsible AI, privacy, security, governance, auditability, and AI risk controls.
- Comfort engaging senior stakeholders while remaining technically credible with engineering teams.
- Hands-on experience with Python, APIs, backend engineering, cloud-native systems, LLMs, RAG, embeddings, prompt/context engineering, MLOps/LLMOps, deployment, monitoring, evaluation, guardrails, Docker, and Kubernetes.
Role scope
- Lead the design, build, and deployment of production-grade AI platforms and solutions.
- Lead or mentor engineering teams, shape technical roadmaps, and own AI engineering delivery.
- Manage technical delivery, set engineering standards, and review implementation quality.
- Deliver secure and scalable AI systems in a complex enterprise environment.
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