Production ML Engineer: AWS SageMaker & Banking

Client of Salt; Dubai; On-site; AED 350,000 - 600,000; Full time

Job summary

Salt is seeking a skilled ML Engineer bridging Data Science and MLOps to design, develop, and productionize machine learning solutions for banking use cases in Dubai. The role emphasizes hands-on Python engineering, model delivery, and end-to-end lifecycle from problem framing to deployment monitoring.

You will build end-to-end pipelines on AWS (SageMaker), manage versioning and release processes, and collaborate with data scientists, data engineers, and security teams to meet regulatory

Qualifications

• Strong hands-on Python for machine learning and production-quality software development.
• Solid knowledge of supervised and unsupervised learning, feature engineering, model selection, cross-validation, and performance metrics.
• Practical experience with common ML libraries such as scikit-learn, XGBoost, LightGBM, pandas and NumPy.
• Good SQL skills and experience working with large, complex datasets.
• Experience productionizing models, including packaging, APIs, batch inference, containers, and automated pipelines.
• Hands-on AWS knowledge, preferably including SageMaker, S3, ECR, ECS/EKS or Lambda, Step Functions and CloudWatch.
• Experience with Git-based development, code reviews, automated testing, and CI/CD fundamentals.
• Understanding of model monitoring, data/model drift, explainability, reproducibility and model lifecycle governance.
• Ability to communicate technical findings clearly to both technical and business stakeholders.

Responsibilities

• Design, develop and validate machine learning models for banking and customer-focused use cases.
• Convert notebooks and prototypes into modular, tested, maintainable Python code for production.
• Build reproducible training and inference pipelines for batch and real-time use cases.
• Deploy models on AWS using managed services, containers, APIs, or scheduled processing patterns.
• Implement model versioning, experiment tracking, release gates and governance with MLOps teams.
• Monitor model performance, data quality, drift, service health, and business outcomes; support retraining as needed.

Skills

Python for ML

Model deployment

Explainability

SQL

scikit-learn

XGBoost

LightGBM

pandas

NumPy

Tools

SageMaker

Docker

Kubernetes

Git

CI/CD

Job description

Salt is seeking a skilled ML Engineer bridging Data Science and MLOps to design, develop, and productionize machine learning solutions for banking use cases in Dubai. The role emphasizes hands-on Python engineering, model delivery, and end-to-end lifecycle from problem framing to deployment monitoring.

You will build end-to-end pipelines on AWS (SageMaker), manage versioning and release processes, and collaborate with data scientists, data engineers, and security teams to meet regulatory