Job Overview
Senior Machine Learning Engineer – Intelligent Financial Risk & Fraud Analytics
Location: United Arab Emirates – Dubai Advanced Fintech Innovation District
Executive Overview
A highly sophisticated digital finance ecosystem driving next-generation banking intelligence, AI-powered fraud prevention, and enterprise financial analytics modernization is seeking an elite Senior Machine Learning Engineer to design, develop, and operationalize advanced machine learning systems supporting critical fintech operations across regional and international markets.
This strategic role is designed for technically exceptional ML professionals capable of building large-scale artificial intelligence infrastructures powering fraud detection engines, transaction intelligence systems, customer behavioral analytics, credit risk automation platforms, intelligent financial forecasting models, and real-time decisioning ecosystems for enterprise fintech environments.
The successful candidate will join a high-performance engineering and data science division responsible for transforming complex financial data into intelligent predictive systems capable of supporting millions of high-value digital financial interactions daily.
This environment demands deep expertise in machine learning engineering, MLOps automation, distributed computing, cloud-native AI infrastructure, financial data modeling, and production-grade AI deployment at enterprise scale.
Core Responsibilities
Enterprise Machine Learning Engineering
- Design, build, and deploy advanced machine learning systems for:
- Fraud detection
- Transaction anomaly detection
- Credit scoring automation
- Customer financial behavior prediction
- Financial recommendation engines
- AML intelligence systems
- Real-time payment risk analysis
- Develop scalable AI pipelines supporting high-volume fintech operations.
- Engineer low-latency inference systems for real-time financial decision-making.
- Optimize predictive model performance for accuracy, scalability, and operational efficiency.
AI Infrastructure & MLOps
- Build end-to-end MLOps frameworks for automated model training, deployment, monitoring, and governance.
- Develop containerized machine learning services operating within cloud-native fintech ecosystems.
- Implement feature engineering and feature store infrastructures for enterprise-scale AI systems.
- Build automated model retraining and drift detection mechanisms.
- Support AI observability and model governance frameworks.
Data Science & Financial Intelligence Collaboration
- Partner with data scientists, software engineers, cybersecurity teams, and fintech strategists.
- Transform large-scale structured and unstructured financial datasets into production-ready AI models.
- Conduct advanced exploratory data analysis and predictive modeling initiatives.
- Enhance AI-driven fraud mitigation and transaction intelligence capabilities.
AI Governance & Compliance Support
- Support responsible AI governance and explainability initiatives.
- Develop interpretable machine learning systems for regulated fintech environments.
- Contribute to enterprise AI risk management frameworks.
- Ensure AI systems align with operational resilience and data governance requirements.
Technology Environment
Candidates will operate within advanced ecosystems involving:
- Python
- TensorFlow
- PyTorch
- Scikit-learn
- XGBoost
- Apache Spark
- MLflow
- Kubernetes
- Docker
- Kafka
- Redis
- Airflow
- Databricks
- AWS SageMaker
- Google Vertex AI
- Snowflake
- Feature store architectures
- Real-time event streaming systems
- AI-powered financial intelligence platforms
Required Qualifications
Educational Background
- Bachelor’s or Master’s degree in:
- Machine Learning
- Artificial Intelligence
- Computer Science
- Data Science
- Statistics
- Applied Mathematics
- Software Engineering
Professional Experience
- 5–9 years of experience in machine learning engineering or AI systems development.
- Strong fintech, banking, payment technology, or financial analytics exposure.
- Proven experience deploying ML systems into enterprise production environments.
- Experience handling large-scale financial datasets and transaction intelligence systems.
Technical Expertise
Strong command of:
- Supervised and unsupervised learning
- Deep learning architectures
- Real-time inference systems
- Feature engineering
- Distributed computing
- Model deployment automation
- Cloud-native AI infrastructure
- MLOps pipelines
- AI observability and monitoring
Preferred Qualifications
- Experience with fraud analytics or AML systems.
- Exposure to AI governance and explainable AI frameworks.
- Knowledge of graph-based machine learning for financial intelligence.
- Cloud certifications or MLOps certifications.
Compensation & Strategic Benefits
- Premium tax-efficient GCC salary package
- Executive housing and relocation support
- Annual AI innovation incentives
- International machine learning conference sponsorship
- Family healthcare and wellness coverage
- Access to elite fintech AI laboratories
- Long-term technical leadership progression
- Participation in enterprise-scale financial AI transformation programs
Are you excited about this opportunity?
Don’t miss the chance to make a difference in the fintech and FX industry!
Apply now by clicking on the “Apply Now” button below.
Let’s shape the future of finance together!
#EmploySolutionJobs #FXCareers.
