VP, Machine Learning.MGN Egypt

AI in this role

ml-ops

Job Purpose

Lead the development, deployment, and optimization of ML solutions across the organization
Own and scale ML engineering teams and platforms
Ensure alignment with business goals and technical excellence
Drive innovation, efficiency, and enterprise ML adoption

Key Result Areas / Responsibilities

Lead design, development, and deployment of scalable ML systems and platforms
Drive ML engineering best practices, including lifecycle management
Collaborate with Data Science, Product, and Engineering teams
Establish MLOps, CI/CD pipelines, and model monitoring standards
Mentor and grow ML engineering teams

Operating Environment

Works in a cloud-native environment with modern ML frameworks
Collaborates cross-functionally with:
- Data Scientists
- Software Engineers
- Product Managers
- Business stakeholders

Decision Authority

Own ML engineering roadmap and technology stack
Responsible for:
- Hiring and team structure
- Architecture decisions
- Delivery quality and timelines
Decide on vendor and tooling strategy

Skills & Experience

10+ years engineering experience, including ML leadership
Proven experience deploying ML models at scale in production
Expertise in:
- ML frameworks
- On-prem ML platform and Cloud platforms (Azure/AWS/GCP)
- MLOps, CI/CD, containerization (Docker, Kubernetes)
Strong leadership, stakeholder management, and communication skills
Understanding of ethical AI, privacy, and compliance

How we rate this

VP, Machine Learning.MGN Egypt at Mashreq rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML Ops

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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