AI Lead Engineer
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About the Role
We are looking for AI Lead Engineer to join one of our clients team in Abu Dhabi, United Arab Emirates. Purpose of the role: To lead the AI architecture function for digital transformation projects, serving as the strategic authority for both cloud-native and on-premise AI ecosystems.
Key Skills for This Role
Full Job Posting
Overview
We are looking for
Ai Lead Engineer
to join one of our clients team in Abu Dhabi, United Arab Emirates.
Purpose of the role:
To lead the
AI architecture
function for digital transformation projects, serving as the strategic authority for both cloud-native and on-premise AI ecosystems.
The role requires advanced technical mastery in Agentic-AI, Generative AI, Machine Learning, MLOps, AgentOps, LLMOps, LLM Finetuning, alongside the consultancy skills to assess AI opportunities, design high-performance AI stacks, and estimate cost-to-value ratios.
The Lead will ensure that AI solutions are seamlessly integrated into the enterprise fabric, including Dynamics 365 and existing data ecosystems.
Key Responsibilities
- AI Strategy & Consultancy: Act as a senior consultant to identify and assess AI use cases, evaluating them for business value, technical feasibility, and architectural fit within the digital transformation roadmap.
- Architecture Design: Architect and oversee the deployment of end-to-end AI stacks using Azure AI Foundry and on-premise enterprise solutions. This includes designing for Inference Engines, Vector Databases, and Agent Builders.
- Sizing & Cost Estimation: Perform detailed technical sizing of AI infrastructure (Compute/GPU/Token usage) and provide accurate cost-benefit analysis for both Azure cloud and on-premise implementations.
- Agentic & Gen-AI Leadership: Lead the design and implementation of Agentic-AI workflows and Multi-Agent Systems, ensuring robust orchestration and goal-alignment. LLM Finetuning for Arabic language is also needed.
- Operations (MLOps/LLMOps/AgentOps): Establish and enforce standards for the AI lifecycle, focusing on automated deployment, monitoring of agentic behavior, and model performance optimization.
- Integration: Design architectures that bridge on-premise data security with Azure AI capabilities, ensuring seamless data flow between the AI stack and the Medallion data architecture.
- Compliance & Governance: Ensure all AI architectures comply with federal AI ethics guidelines, focusing on data privacy and responsible AI.
- Technical Troubleshooting: Resolve complex issues related to model latency, vector embedding quality, and integration bottlenecks within the AI pipeline.
- Leadership & Mentoring: Guide AI engineers and data scientists, fostering a culture of excellence in all data activities.
Requirements
- Certifications: Microsoft Azure AI Engineer Associate and Azure Solutions Architect Expert certification.
- Experience: Minimum of 8 years in AI/Machine Learning, with at least 3 years in a Lead Architect capacity for large-scale enterprise projects.
- Hands-on Mastery: Deep experience with Azure AI Foundry, Vector Databases (e.g., Weaviate, Azure AI Search, vLLM), and Inference Engines (e.g., Nvidia Triton, vLLM, etc)
- Advanced AI Paradigms: Proven track record in delivering Agentic-AI and Generative AI solutions at an enterprise level.
- Technical Stack: Proficiency in AI and ML frameworks, Python, Agentic Workflow, and containerization (Kubernetes/Docker).
- Consultancy Skills: Strong ability to translate complex AI concepts into architectural blueprints and financial estimates for stakeholders?
- 6 + years relevant exp. ideally working with consulting firms, big 4 or government entities
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- Language:
- Proficiency in Arabic
- and English is required
Competencies
- AI Technical Leadership: Ability to set the vision for the AI stack.
- Financial Acumen: Proficiency in AI cost modeling (Tokenomics & Infrastructure).
- Analytical Thinking: Breaking down business problems into AI-solvable components.
- Collaboration: Working closely with the Data Architect Lead to leverage enterprise data for AI.
- Adaptability: Staying ahead of the rapidly evolving AI landscape
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