Build and maintain automated test coverage (including regression and integration testing) and support continuous improvement in engineering standards and quality controls.
Build and maintain automated test coverage (including regression and integration testing) and support continuous improvement in engineering standards and quality controls.
Performance & Optimization: Lead efforts in system performance monitoring, tuning, and optimization, ensuring high availability, low latency, and efficient resource utilization. Identify and resolve system-wide issues in databases, caching layers, message queues, and other backend components.
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.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
Evaluate inference engines (vLLM, TensorRT-LLM, Triton Inference Server) and publish recommendations balancing performance, cost, and resilience. Provide hands-on support to agency teams applying these to their workloads.
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.
Model Integration & Optimization: Integrate state-of-the-art LLMs and foundational models via commercial APIs or self-hosted inference setups. Implement strategies to manage model latency, token constraints, context windows, and fallback mechanisms.