Machine Learning Engineer

TamaraDubai, United Arab Emirates1w ago

In Office Senior EMEA Cloud Computing Fintech Artificial Intelligence Machine Learning Engineer ML Engineer Ray Airflow Prefect MLOps Python

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Responsibilities

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.
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.