Job at a Glance
- 📍 Location: Dubai, United Arab Emirates
- 🏢 Company: Discovered MENA
- 💼 Employment Type: Full-time
- 📊 Seniority: Mid-Senior level
- 🏭 Industry: Financial Services and Technology, Information and Media
- 📅 Date Posted: 2026-08-07
- 🌐 Category: Artificial Intelligence
About the Role & Company
Discovered MENA has an open Senior Lead – AI & Data Innovation role based in Dubai, United Arab Emirates. This is a Artificial Intelligence seat inside a team that’s shaping how the function scales locally. In practice, that means faster feedback loops, more visible ownership, and less waiting your turn. The scope reads like a standard Senior Lead – AI & Data Innovation brief on paper; in a growing team it usually stretches wider than the JD. GCC demand for artificial intelligence skills is still healthy, and Dubai continues to sit at the top of the regional pay bands. For someone weighing a move, this is the kind of seat that adds GCC experience to a CV without locking you in — useful whether you stay or move on later.
Key Requirements
- 8+ years of engineering experience, with a strong track record of leading innovation in AI or data
- on hands-on experience with Python, distributed data systems (e
- Proven experience architecting, deploying, and scaling production
- Demonstrated ability to think creatively, challenge conventions, and lead R&D from 0 to 1
- Hands-on time with deep understanding of the current AI research landscape and how to translate cutting
Job Description
we’re looking for a Senior Tech Lead with exceptional depth in AI, data systems, and product innovation.
This is not a traditional software engineering management role. we’re hiring a high-quality thinker and builder who is engaged about challenging legacy incumbents, leading deep technical exploration, and shipping real products that change the game.
you’ll lead forward-looking R&D efforts, explore and prototype breakthrough ideas, and set the technical direction as this company scales across the MENA region. Working directly with the founders, you’ll have a primary voice in shaping not only what we build, but how we think.
Lead Technical Innovation: Identify whitespace opportunities, challenge legacy solutions, and architect future-ready systems for credit intelligence.
Prototype & Build: Translate abstract concepts into high-impact prototypes and production-grade systems using AI and real-time data infrastructure.
Set Technical Direction: Own the architectural roadmap and strategic vision for R&D initiatives with autonomy and clarity.
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this Senior Lead – AI & Data Innovation opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, TensorFlow, PyTorch, Machine Learning, Deep Learning, NLP, LLM, Computer Vision, MLOps, Data Pipelines. ATS parsers match exact strings, not synonyms.
- Use a single-column layout. Two-column resumes break parsers — text gets scrambled or dropped.
- Save as .docx or standard .pdf (not scanned/image PDF). Avoid text boxes, headers, footers, graphics.
- Use standard section headings: Experience, Education, Skills, Certifications.
- Mirror the exact job title (Senior Lead – AI & Data Innovation) in your target role line where truthful.
- Quantify results with numbers, percentages, currency. Metrics improve ATS scoring and human review.
- Spell acronyms out once (e.g., Search Engine Optimization (SEO)) so parser catches both forms.
- Font: Arial, Calibri, or Times New Roman at 10-12pt. Fancy fonts can render as unreadable glyphs.
Top 10 Interview Questions for This Role
- Walk me through your background and why you’re interested in this role.
- Describe a challenging project you owned end-to-end. What was the outcome?
- Tell me about a time you disagreed with a stakeholder. How did you handle it?
- How do you prioritize when everything on your plate feels urgent?
- How do you decide between fine-tuning a model vs. prompt engineering?
- Explain overfitting to a non-technical stakeholder.
- Walk through your data validation pipeline for training data.
- How do you evaluate model performance beyond accuracy?
- Describe your MLOps setup — from experimentation to production.
- How do you handle model drift once a system is in production?
How to Apply
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