Create the unified platform where users discover, evaluate, access, and manage available services, tools, and applications from a single interface – web-based, CLIs, or even MCPs
Work closely with ML engineers to proactively identify and resolve technical challenges , while also contributing directly to the implementation of those improvements. This includes troubleshooting and optimizing machine learning workflows for maximum efficiency and performance, architecting and executing cutting-edge technologies/methodologies for accelerating ML development end-to-end from concept to deployment & maintenance.
Leading engineer role for all global contact center applications; including - cloud providers, internal/external infrastructure, telephone providers, interaction recording, workforce management, reporting, session border controllers, and integrated equipment, devices, and infrastructure.
Own assigned deliverables throughout the software development lifecycle, including design, implementation, integration, troubleshooting, and deployment.
Develop and apply learning-based planning techniques (e.g. reinforcement and imitation learning) to enhance navigation adaptability and robustness in dynamic environments
Establish the runtime and governance framework for AI Agents, including execution isolation, permission control, auditability, evaluation, stability, and cost governance.
Innovation & R&D: Evaluate emerging technologies, frameworks, and methodologies, conducting proof-of-concepts (PoCs) to strategically introduce them into our programme.
We are looking for an experienced backend engineer to join our Global Payments Engineering team. In this role, you will design and build scalable payment infrastructure that powers cross-border payment solutions across multiple markets. Working closely with regional Product, Business, and Compliance teams, you will deliver localized payment capabilities, integrate with global payment partners, and help drive our international business expansion.
Work closely with Product Managers, Business Development, data teams, and regional stakeholders to translate growth objectives into practical engineering solutions.
AI Service Enablement: Enable enterprise-wide adoption of AI capabilities by developing reusable APIs, SDKs, templates and shared services that simplify AI integration and accelerate solution delivery across teams, while maintaining AI security and compliance within the organisation.