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AI Technical Lead/AI Solution Architect
Cognitive
Dubai, UAE
Full-time
Mid-Senior
Onsite
Discovered 3 weeks ago
AI/ML solution deliveryMachine LearningGenerative AI and LLMsAgentic AIPythonRetrieval-augmented generation (RAG)
Free
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AI/ML solution deliveryMachine LearningGenerative AI and LLMs
Role Overview
Design and deliver production-grade AI/ML solutions in government or highly regulated environments.
Combine hands-on AI engineering, solution architecture, Generative AI expertise, and technical leadership.
Define technical solutions, guide implementation, review architecture and code, and technically lead AI delivery teams.
Key Skills for This Role
AI/ML solution deliveryMachine LearningGenerative AI and LLMsAgentic AIPythonRetrieval-augmented generation (RAG)
Full Job Posting
Role Overview
Design and deliver production-grade AI/ML solutions in government or highly regulated environments.
Combine hands-on AI engineering, solution architecture, Generative AI expertise, and technical leadership.
Define technical solutions, guide implementation, review architecture and code, and technically lead AI delivery teams.
Key Requirements
- Proven hands-on experience designing and delivering AI/ML solutions into production.
- Strong practical knowledge of Machine Learning, Generative AI/LLMs, and Agentic AI.
- Strong Python development skills for prototyping, reviewing, and guiding production AI/ML implementations.
- Practical experience with RAG, embeddings, vector search, prompt engineering, tool/function calling, agent orchestration, and LLM evaluation.
- Strong AI solution architecture and integration capabilities covering scalability, security, reliability, performance, and cost.
- Experience designing AI solutions across public, hybrid, private, or sovereign cloud environments.
- Strong Microsoft Azure experience, ideally including Azure OpenAI, Azure AI Foundry, Azure AI Search, and Azure Machine Learning.
- Understanding of MLOps/LLMOps, including deployment, evaluation, monitoring, versioning, and production lifecycle management.
- Strong understanding of AI security, governance, responsible AI, data privacy, and data sovereignty.
- Experience with APIs, microservices, containers, Kubernetes, and CI/CD.
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