Lead the Generative AI Platform team, defining strategy and roadmap for the GenAI, Data, and ML Platforms. Drive adoption through strong internal partnerships and enablement programs. Embed privacy, security, compliance, and Responsible AI controls into the platform.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
About the job:
Senior Director, Machine Learning and AI (Dubai based - relocation + visa sponsorship provided)
Company Description
As part of the Delivery Hero Network, Talabat is the pioneer of the online food ordering industry in the Middle East. Our operations span across Kuwait, Egypt, KSA, Bahrain, Oman, Qatar, Jordan and the United Arab Emirates. With more than 6,500+ employees, 4 million users, 80,000+ brands and 1,1 million, daily orders, we are just getting started.
Role Summary:
We are looking for a Senior Director of Machine Learning to lead our Generative AI Platform team, a strategic, high-visibility platform that is responsible for enabling AI-powered experiences across the company.
The team is deeply customer-obsessed and impact-driven, focused on turning cutting-edge research in LLMs, multimodal learning, generative AI, and agentic automation into products.
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This leader will own the strategy, execution, and adoption of our GenAI platform ecosystem, including no-code/low-code tooling, SDKs, agent frameworks, governance, and enterprise-wide integrations across all markets.
The ideal candidate is a hands-on, platform-minded technologist who can balance rapid iteration with strong governance, and who can partner deeply across engineering, product, data, legal, and security.
What's On Your Plate?
- Define the multi-year strategy and roadmap for the GenAI, Data, and ML Platforms
- Lead and grow a team of exceptional AI/ML engineers developing production-grade machine learning and generative AI solutions for the
- Drive a unified, scalable, vendor-agnostic architecture for LLM model access, prompt orchestration, knowledge retrieval, agent evaluations, agentic workflows, and enterprise integration
- Drive adoption through strong internal partnerships, enablement programs, documentation, templates, training, and best practices
- Build a healthy ecosystem of reusable agents, tools, MCP servers, evaluation frameworks, and shared components used across the company
- Own the end-to-end lifecycle of ML systems — from ideation and experimentation to productionization and continuous improvement.
- Enable measurable business outcomes in employee productivity, customer experience, and developer acceleration
- Establish strategic partnerships with vendors and manage vendor contracts
- Embed privacy, security, compliance, and Responsible AI controls into platform
- Lead and grow a world-class engineering team focused on platform excellence
- Build a culture of innovation, quality, ownership, and speed
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What Did We Order?
- 10+ years of industry experience in machine learning and AI engineering, including 4+ years of leadership experience managing applied scientists and ML engineers.
- A proven track record of building and deploying AI/ML systems at scale, with shipped AI-powered products that have reached millions of users.
- Proven track record driving cross-org initiatives; expert in roadmap planning, dependency management, and stakeholder alignment with Product and TPM partners
- Deep understanding of LLMs, vector stores, retrieval, prompt engineering, agent frameworks (LangGraph, LlamaIndex, Strands, etc.), orchestration, agent evaluation and RAG
- Depth in one or more: distributed systems, low latency services, model serving/inference, cloud cost optimization
- Track record recruiting and developing diverse engineering talent and building inclusive, high-performance cultures
- Exceptional communication and influence skills, translates technical strategy into measurable business impact at the executive level
- Broad knowledge of machine learning and technical depth in one or more areas: AI, recommendation systems, natural language processing, generative models, or large-scale agent systems.
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