Machine Learning Engineer at Tamara

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

01 Jan, 26

Salary

0.0

Posted On

03 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Pandas, NumPy, Scikit-learn, PySpark, FastAPI, Flask, Distributed Computing, MLOps, AWS, GCP, Azure, Docker, Kubernetes, Kafka, Kinesis

Industry

Financial Services

Description

About Us 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 Model Deployment & Scaling 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. Model Monitoring & Maintenance 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. Strong Python skills (including Pandas, NumPy, scikit-learn, PySpark, FastAPI/Flask). 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.

Responsibilities

You will design, build, deploy, and scale ML models that drive credit risk and fraud prevention. Collaborating with data scientists and engineering teams, you will transform research prototypes into production-grade solutions.