Applied Machine Learning Architect

About Alaan

Alaan is the SuperCard™ for businesses and the most loved fintech in the Middle East. Our mission is to simplify finance for businesses so they can save time and money.

Alaan provides everything businesses need to manage and control expenses, including the SuperCard™, AI-powered automation and insights, streamlined accounting, and centralized dashboards, all in one place.

Over 2000 businesses in the UAE and KSA, from startups to enterprises, use Alaan to control spending and save costs.

Backed by Y Combinator and top global investors- including founders and executives of leading startups - Alaan is built by a world-class team from McKinsey, BCG, Goldman Sachs, Barclays, Zomato, Careem, Rippling, and other high-growth companies.

We’re not just building software. We’re reimagining how finance works for modern businesses across the region.

We’re not just building software. We’re reimagining how finance works for modern businesses across the region.

About the role

At Alaan, we’re building the modern financial stack for the Middle East—empowering businesses to manage cards, payments, and expenses with speed, intelligence, and trust. AI is already deeply embedded in our product, powering critical customer workflows. Now, we’re scaling our AI and ML engineering team to ensure intelligence sits at the heart of every problem we solve.

We’re looking for an Applied Machine Learning Architect who will own the full lifecycle of ML features - from designing data pipelines to building, evaluating, deploying, and maintaining high-performance models. You’ll work closely with AI Product, Data Science, and Engineering teams to turn promising prototypes into scalable, reliable, and explainable features that deliver measurable business impact.

What you'll do

• Model Development & Deployment : Build, fine-tune, and evaluate models - including supervised learning, NLP, LLMs, and retrieval systems—for real-world finance workflows. Design experiments and offline / online evaluations to validate performance, optimizing for accuracy, latency, scalability, and cost.
• Data Engineering & Pipelines : Develop and maintain robust data pipelines for both training and inference. Ensure data quality, labeling processes, and feature engineering are production-ready.
• Productionization : Implement model versioning, CI / CD for ML, monitoring, and drift detection. Create alerting and automated retraining workflows to sustain performance at scale.
• Collaboration & Delivery : Partner with product managers and designers to ensure ML solutions address customer needs. Work with backend engineers to integrate ML services into product workflows, and document systems, decisions, and learnings for transparency and reusability.

What we are looking for

What's in it for you