AI DevOps / Cloud Engineer (MLOps)

AI in this role

mlflowweights-and-biasessagemakerdatabricks

ml-ops

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
Manage integrations from CDPs and product analytics platforms including Segment and Amplitude, and mobile attribution and engagement tools including Adjust, Firebase, MoEngage, and JourneyFi into the central AWS data infrastructure
Support deployment, access management, and integration of metadata management and BI tools including OpenMetadata and Metabase within the cloud environment
Ensure all cloud infrastructure and AI systems meet security and compliance standards, including secrets management, encryption, network security, and access controls
Maintain clear, up-to-date documentation for all infrastructure, deployment processes, and operational runbooks. Set engineering standards that the AI team follows as it grows

Requirements

5 to 10 or more years of experience in DevOps, Cloud Engineering, or Site Reliability Engineering
Proven experience building cloud infrastructure from scratch, not solely maintaining existing environments
Expert-level AWS skills are required and non-negotiable
Hands-on experience with Kubernetes in production environments
Experience with Infrastructure as Code using Terraform or CloudFormation
Comfortable being the sole infrastructure owner on a team initially, with a strong ownership mindset
Able to work directly with data scientists and ML engineers and translate their needs into infrastructure
Experience in high-growth startups, scale-ups, or product companies is preferred
Background in AI/ML infrastructure or MLOps is a strong advantage
Azure familiarity is a plus

Technical Skills

Cloud and Infrastructure: AWS (EC2, EKS, S3, RDS, Lambda, SageMaker, IAM, VPC, CloudWatch), Terraform, CloudFormation; Azure familiarity is a plus

DevOps and CI/CD: GitHub Actions, GitLab CI, Git, GitOps principles, automated testing and deployment frameworks

Containerisation and Orchestration: Docker, Kubernetes (EKS), Helm charts

MLOps: MLflow, Weights and Biases, Airflow, Dagster, Prefect, Evidently AI, Arize, AWS SageMaker

Monitoring and Observability: Datadog, Prometheus, Grafana, CloudWatch, ELK Stack, PagerDuty

Security: IAM, Secrets Manager, KMS, VPN, zero-trust principles, encryption and access controls

Data and Integration: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, S3-based Lakehouse, Metabase, OpenMetadata, Segment, Amplitude, Adjust, Firebase, MoEngage, JourneyFi, Kafka, RabbitMQ

Why Join Us?

Work with one of the world’s leading financial derivatives institutions.
Competitive salary plus performance-based incentives.
Access to a dynamic, international, and fast-growing environment.
Strong opportunities for career progression within a global financial group.
Be part of a business committed to innovation, excellence, and long-term growth.

Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.

MultiBank Group is an equal opportunity employer. We welcome applications from candidates of all backgrounds and do not discriminate on the basis of nationality, gender, age, religion, or disability.

How we rate this

AI DevOps / Cloud Engineer (MLOps) at Multibank Group rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsMlflowWeights And BiasesSagemakerDatabricks

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used Mlflow in your day-to-day work.
What are the limits of Weights And Biases that you've run into, and how did you work around them?
What's a project where you used Sagemaker hands-on?
Walk me through how you've used Databricks in your day-to-day work.

Adapt your resume

List these exact terms on your resume: ML Ops, Mlflow, Weights And Biases, Sagemaker, and Databricks. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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