Job Overview
ENGINEER — AI Infrastructure for Financial Services
Location: Riyadh, Saudi Arabia
Seniority Level: Senior / Principal Engineering
Employment Type: Full-time, Permanent
Industry Focus: Fintech, Artificial Intelligence, Machine Learning Infrastructure, Cloud, Financial Technology
Engineering Mission
We are looking for an exceptional Engineer to build the infrastructure required to deploy artificial-intelligence systems reliably across financial applications.
The role sits beneath the AI product layer, focusing on data pipelines, model-serving infrastructure, scalable computing, observability and secure production environments.
The successful candidate will help bridge experimental machine learning with dependable, enterprise-grade financial technology.
Engineering Responsibilities
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Build scalable infrastructure for production machine-learning workloads.
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Develop data pipelines supporting financial AI applications.
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Design model-serving environments capable of handling high transaction volumes.
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Implement automated deployment pipelines for machine-learning systems.
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Establish monitoring for model, infrastructure and application performance.
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Optimize computing resources for cost, latency and scalability.
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Collaborate with data scientists to operationalize analytical models.
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Build APIs connecting AI services with financial applications.
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Implement secure data-processing environments.
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Develop resilient infrastructure for mission-critical financial workloads.
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Diagnose production failures and conduct root-cause analysis.
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Establish automated testing and deployment standards.
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Support cloud-native modernization initiatives.
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Document infrastructure architecture and operational procedures.
Required Technical Background
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Bachelor’s degree in Computer Science, Software Engineering, Data Engineering or a related field.
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5+ years of professional engineering experience.
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Strong Python plus experience with Java, Go or another production language.
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Experience with AWS, Azure, Google Cloud or comparable platforms.
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Strong knowledge of Docker, Kubernetes, CI/CD and Infrastructure-as-Code.
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Experience with data pipelines and distributed systems.
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Familiarity with machine-learning operations and model deployment.
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Understanding of security and privacy requirements for financial data.
Technical Environment
MLOps • Cloud • Kubernetes • Python • Data Pipelines • APIs • Distributed Systems • Model Serving • CI/CD • Infrastructure Automation
Engineering Standard
The Engineer will turn AI from a laboratory capability into reliable financial infrastructure—observable, scalable, secure and capable of operating continuously under demanding production conditions.
Don’t miss the chance to make a difference in the fintech and FX industry!
Apply now by clicking on the “Apply Now” button below.
Let’s shape the future of finance together!
#EmploySolutionJobs #FXCareers.
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