Welcome to the company, a global financial pioneer established in 2005, now 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, we are committed to innovation, excellence, and helping our clients achieve their financial goals.
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We are seeking an AI DevOps and Cloud Engineer with MLOps capability to build and own the infrastructure backbone for our AI Initiative. This foundational role requires 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 role is ideal 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 Initiative on AWS, including compute, networking, storage, IAM, and cost management.
- 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.
- Own Docker and Kubernetes across all AI workloads, managing containerized environments for model training, batch processing, and real-time inference.
- Work closely with ML engineers to deploy models into production, building and maintaining model serving infrastructure, inference endpoints, and batch scoring pipelines.
- Set up and manage workflow orchestration tools such as Airflow, Dagster, or Prefect for data and ML pipeline scheduling and monitoring.
- Implement end-to-end observability across infrastructure, application performance, and ML model health, including alerting and dashboards.
- Evolve the MLOps practice over time, implementing drift detection, experiment tracking, data quality checks, and automated retraining triggers.
- Manage integrations with data and analytics platforms including Segment, Amplitude, Adjust, Firebase, and MoEngage into the central AWS data infrastructure.
- Maintain clear documentation for all infrastructure, deployment processes, and operational runbooks.
- Ensure all cloud infrastructure and AI systems meet security and compliance standards, including secrets management, encryption, and access controls.
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.
- Hands-on experience with Kubernetes in production environments.
- Experience with Infrastructure as Code using Terraform or CloudFormation.
- Familiarity with ML workloads and the ML lifecycle.
- Experience with MLOps tooling is an advantage.
- Background in AI/ML infrastructure or high-growth product companies is preferred.
- Strong ownership mindset with the ability to operate as the sole infrastructure lead initially.
- 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.
- Containerization 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 the company, dedicated to excellence, innovation, and shaping the future of finance.
The company 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.
