Machine Learning Engineer

Tamara; Dubai; On-site; AED 250,000 - 350,000; Full time

Job summary

A leading fintech platform in the UAE is seeking a Senior Machine Learning Engineer to join their Risk Data Science team. The successful candidate will design and deploy ML models for credit risk and fraud prevention, requiring 6+ years of experience and expertise in cloud environments. Responsibilities include productionising models, building APIs, and collaborating closely with data scientists to optimize performances. This is a chance to work at the forefront of fintech innovation with a focus on real-time decision systems.

Qualifications

• 6+ years of experience as an MLE, ML Engineer, or MLOps Developer.
• Proficiency in distributed computing frameworks like Spark or Ray.
• Experience with MLOps tools such as MLflow or SageMaker.

Responsibilities

• Productionise risk and fraud models using efficient architectures.
• Design APIs and pipelines for real-time model inference.
• Build and maintain CI/CD pipelines for model deployment.

Skills

Distributed computing frameworks (Spark, Ray)

Workflow orchestration tools (Airflow, Prefect)

MLOps tools (MLflow, SageMaker, Vertex AI)

Model deployment in cloud environments (AWS/GCP/Azure)

Microservice architecture

Containerization (Docker)

Orchestration (Kubernetes)

Real-time streaming systems (Kafka, Kinesis, Pub/Sub)

Job description

Tamara is the leading fintech platform in Saudi Arabia and the wider GCC region with a mission to help people make their dreams come true by building the most customer‑centric financial super‑app on earth. The company serves millions of users in the region and partners with leading global and regional brands such as SHEIN, Jarir, noon, IKEA and Amazon, as well as small and medium businesses.

Tamara is Saudi Arabia’s first fintech unicorn and is backed by Sanabil Investments, a wholly owned company by the Public Investment Fund (PIF), SNB Capital, Checkout.com, amongst others. The company operates from its headquarters in Riyadh, with additional regional and global support offices.

Your Role

We are looking for a Senior Machine Learning Engineer (MLE) to join our Risk Data Science team. You will play a key role in designing, building, deploying, and scaling ML models that drive credit risk, fraud prevention, behavioral scoring, and other risk‑related decision systems across our business.

You will work closely with data scientists, risk analysts, and engineering teams to transform research prototypes into high‑performance, production‑grade solutions that operate at scale in real‑time decisioning environments.

Your Responsibilities

• Productionise risk and fraud models developed by the DS team using robust, efficient, and maintainable architectures
• Design low‑latency, high‑availability APIs and pipelines for real‑time model inference.
• Implement batch scoring systems for periodic risk assessments.

MLOps & Infrastructure

• Build and maintain CI/CD pipelines for model deployment and monitoring.
• Set up automated feature engineering pipelines, leveraging feature stores.
• Ensure model governance: reproducibility, versioning, auditability, and compliance with regulatory requirements.
• Implement real‑time and batch monitoring for data drift, concept drift, and model performance.
• Build automated retraining workflows and model rollback mechanisms.

Collaboration with Risk DS

• Work closely with risk data scientists to translate experimental code (Python, notebooks) into production‑grade services.
• Advise DS on efficient model architectures for operational environments.
• Optimize feature computation for speed and scalability.

System Design & Integration

• Integrate models with credit underwriting, fraud detection, collections, and merchant risk systems.
• Collaborate with backend engineering to align on API contracts and system interfaces.

Your Expertise

• 6+ years of experience as an MLE, ML Engineer, Mlops Developer.
• Proficiency in distributed computing frameworks (Spark, Ray) and workflow orchestration tools (Airflow, Prefect).
• Experience with MLOps tools (MLflow, SageMaker, Vertex AI, or similar).
• Strong understanding of model deployment in cloud environments (AWS/GCP/Azure).
• Solid knowledge of microservice architecture, containerization (Docker), and orchestration (Kubernetes).
• Proven track record of deploying and maintaining ML models in production at scale.
• Experience in building and integrating with real‑time streaming systems (Kafka, Kinesis, Pub/Sub).

All qualified individuals are encouraged to apply.