Applied Machine Learning Architect

Alaan is the Middle East’s first AI-powered spend management platform, built to help businesses save time and money.
Our all-in-one solution combines smart corporate cards, real-time expense tracking, AI-powered automation, seamless accounting integrations, and deep financial insights- designed to simplify finance operations and maximize control over company spend.

Founded in 2022, Alaan is already the trusted partner of over 2,000 leading businesses across the UAE and KSA, including G42, Careem, McDonald’s, Tabby, Al Barari, Rove Hotels, Rivoli, and CarSwitch. Together, our customers have saved over AED 100 million with Alaan.

In just three years, Alaan has become the #1 expense management platform in the Middle East- and we’ve done it while becoming profitable.

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.

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.

• 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.

• ML & Data Science Fundamentals: You have 5–8 years of experience in ML engineering or applied ML roles, with at least 3 years deploying and maintaining production-grade ML systems. You bring deep expertise in supervised learning, NLP, and LLM techniques, and understand embeddings, vector search, RAG, and transformer architectures. You’re fluent in evaluation metrics such as precision/recall/F1, AUC, and BLEU, and ideally have applied them in fintech, expense management, or workflow automation contexts.
• Engineering Proficiency: You’re highly skilled in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) and experienced with ML pipeline orchestration tools like Airflow, Prefect, or Kubeflow. You’re comfortable working with APIs, microservices, and backend integration, and have deployed models in cloud environments such as AWS, GCP, or Azure.
• Data Infrastructure: You’re fluent in SQL for large-scale data analysis and experienced with vector databases (e.g., Pinecone, Weaviate, FAISS) and other data stores. You may also have worked with feature stores, online learning systems, or real-time inference pipelines.
• ML Ops & Production: You understand best practices for monitoring, logging, and automating retraining workflows. Experience with LLM orchestration frameworks like LangChain or LlamaIndex is a plus, as is exposure to privacy, compliance, and security considerations in ML systems.

• Contribute to building the Middle East’s most beloved fintech brand from the ground up
• Benefit from a role with significant ownership and accountability
• Thrive in a flexible hybrid culture with ample work-life balance
• Participate in exciting offsite events
• Competitive salary and equity
• Enjoy additional perks like travel allowances, gym memberships, and more