
Luxoft
Senior Risk Engineer
- Permanent
- Dubai, United Arab Emirates
- Experience 2 - 5 yrs
Job expiry date: 04/06/2026
Job overview
Date posted
20/04/2026
Location
Dubai, United Arab Emirates
Salary
AED 20,000 - 30,000 per month
Compensation
Job description
The Senior Risk Engineer role is within a global commodity trading company operating in the intersection of commodities risk management, market data systems, and modern software engineering delivery. The role requires hands-on engineering expertise to design, build, and maintain full-stack risk engineering systems that support commodities trading operations and market data processing at scale. The engineer is responsible for translating high-level, often ambiguous business requirements from trading and risk stakeholders into robust, production-ready technical solutions. This includes building systems that handle price risk, mark-to-market valuation, commodities curve modeling, and real-time market data flows. The role operates in a high-velocity engineering environment with multiple daily releases in a CI/CD pipeline, requiring disciplined practices in testing, observability, rollback strategies, and continuous delivery. The Senior Risk Engineer contributes to cloud-native platforms using modern infrastructure practices such as containerization, infrastructure-as-code, and managed cloud services across AWS, GCP, or Azure. The position involves close collaboration with quants, traders, and risk managers to ensure that engineering systems accurately reflect real-world market behavior and risk exposures. The role also includes participation in architectural design decisions, code reviews, and system design improvements, with a strong focus on delivery speed, scalability, and system reliability. The engineer is expected to proactively identify and resolve technical debt while maintaining high-quality engineering standards. Additionally, the role incorporates AI-assisted software development workflows, leveraging tools such as Copilot or Cursor for code generation, test creation, and iterative refinement within fast-release cycles. The position requires strong ownership mindset, ability to operate independently, and capability to deliver complex systems end-to-end without supervision in a fast-paced commodity trading environment.
Required skills
Key responsibilities
- Design, develop, and maintain full-stack risk engineering systems for commodities trading and market data processing
- Translate high-level and ambiguous requirements from trading and risk stakeholders into production-ready technical solutions
- Build and maintain scalable market data pipelines and commodities risk systems including pricing and mark-to-market models
- Implement and support CI/CD pipelines enabling multiple daily production releases with safe deployment practices
- Develop cloud-native applications using AWS, GCP, or Azure with modern infrastructure practices
- Collaborate with quants, traders, and risk managers to ensure systems accurately reflect market and risk behavior
- Participate in system architecture design, code reviews, and technical decision-making processes
- Identify and proactively resolve technical debt and system inefficiencies
- Ensure systems are testable, observable, and resilient under high-frequency deployment environments
- Utilize AI-assisted development tools to improve coding efficiency, testing, and deployment workflows
Experience & skills
- Strong proficiency in Python programming for production systems
- Experience in full-stack software development including backend services, APIs, and frontend interfaces
- Hands-on experience with cloud platforms such as AWS, GCP, or Azure
- Experience with CI/CD pipelines and continuous delivery environments
- Knowledge of containerization and infrastructure-as-code practices
- Understanding of commodities markets including price risk, mark-to-market, and market data flows
- Experience building or maintaining large-scale market data processing systems
- Ability to work with ambiguous requirements and independently deliver solutions
- Experience in observability, testing, and production deployment best practices
- Familiarity with AI-assisted development workflows and tools