ML Ops Engineer: Banking-Grade Production ML

Salt; Dubai; Vor Ort; AED 260.000 - 460.000; Vollzeit

Zusammenfassung

Salt seeks an experienced ML Engineer with strong Python production experience to design, deploy and monitor machine learning models in a regulated banking context. You will bridge data science and MLOps, delivering end-to-end ML use cases from problem framing to production, with AWS SageMaker, containers, APIs and CI/CD pipelines.

You will collaborate with data scientists, data engineers, MLOps, architecture and information security teams to ensure robust, compliant solutions and clear

Qualifikationen

• 6-7+ years in Data Science, ML Engineering or applied ML roles
• Production-ready ML experience in banking or regulated industries preferred
• Strong Python and production software development skills
• Experience delivering end-to-end ML use cases, not just infra

Aufgaben

• Design and validate ML models for banking/customer use cases
• Translate requirements into measurable ML solutions and success criteria
• Prepare data, engineer features, evaluate models and ensure explainability
• Convert notebooks to modular, tested Python code for production
• Build reproducible training and inference pipelines (batch/real-time)
• Deploy models on AWS with managed services, containers, APIs
• Implement versioning, experiment tracking, and release processes with MLOps
• Monitor performance, data quality, drift, and retraining needs
• Create technical documentation covering methodology, deployment and controls
• Comply with security, data privacy, and regulatory requirements
• Collaborate with Data Scientists, Engineers, MLOps, Architecture and InfoSec

Kenntnisse

Python engineering

Productionizing ML

AWS SageMaker

ML model deployment

Model monitoring

SQL data handling

Git CI/CD

Tools

SageMaker

Docker

Kubernetes

Git

Jobbeschreibung

Salt seeks an experienced ML Engineer with strong Python production experience to design, deploy and monitor machine learning models in a regulated banking context. You will bridge data science and MLOps, delivering end-to-end ML use cases from problem framing to production, with AWS SageMaker, containers, APIs and CI/CD pipelines.

You will collaborate with data scientists, data engineers, MLOps, architecture and information security teams to ensure robust, compliant solutions and clear