AI DevOps / Cloud Engineer (MLOps)

About the role

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.

Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.

Role Overview

We are seeking an AI DevOps and Cloud Engineer with MLOps capability to build and own the infrastructure backbone for our AI and Data function. This is a foundational role responsible for designing cloud infrastructure from scratch, establishing CI/CD pipelines for the AI team, managing containerization and orchestration, and evolving the platform into a production-grade MLOps environment as the initiative scales. This is a builder role for someone comfortable owning infrastructure end-to-end in a fast-moving environment.

Key Responsibilities

- Design, build, and own the full cloud infrastructure for the AI and Data function on AWS, including VPCs, IAM, networking, security groups, compute, storage, and cost management. Ensure the foundation is solid before any model goes near production

- Build and maintain CI/CD pipelines for data engineers, ML engineers, and data scientists using GitHub Actions or GitLab CI, implementing GitOps principles and automated deployment workflows. Every release should be automated, auditable, and repeatable

- Own general technology operations for the AI team in the absence of a dedicated TechOps function, including environment setup, access management, developer tooling, system monitoring, incident response, and vendor and license management for all tools used by the team

- Own Docker and Kubernetes across all AI workloads, building scalable, reliable container environments for model training, batch processing, and real-time inference. Manage cluster health, resource allocation, and cost efficiency

- Work closely with ML engineers to deploy models into production, building and maintaining model serving infrastructure, inference endpoints, and batch scoring pipelines. Own the deployment side of the ML lifecycle including packaging, versioning, rollout, and rollback strategies

- Implement end-to-end observability across infrastructure, application performance, and ML model health, including alerting, dashboards, and on-call processes

- Set up and manage workflow orchestration tools such as Airflow, Dagster, or Prefect, ensuring pipelines are reliable, retryable, and observable

- Evolve the MLOps practice over time, implementing drift detection, data quality checks, performance tracking, experiment tracking, and automated retraining triggers