AI-Enabled Application Development: Build and maintain software applications that integrate AI and machine learning capabilities into business processes.
Kubernetes Service Platform Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Kubernetes Service Platform Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
Integrate enterprise applications through authenticated APIs and MCP where appropriate, selecting deterministic workflows, agents or conventional ML based on the problem.
Integrate enterprise applications through authenticated APIs and MCP where appropriate, selecting deterministic workflows, agents or conventional ML based on the problem.
Agentic AI Development: Design and prototype autonomous “agentic” systems capable of analyzing production logs, diagnosing anomalies, and recommending corrective actions to engineers.
Production excellence: Set engineering standards for system design, testing, deployment and observability. Drive improvements in reliability, performance and cost, including the evaluation and productionisation of AI applications.
Production excellence: Set engineering standards for system design, testing, deployment and observability. Drive improvements in reliability, performance and cost, including the evaluation and productionisation of AI applications.
Contribute to cloud infrastructure, CI/CD pipelines, observability and deployment practices across Azure and AWS environments while working closely with Product, UX and Engineering teams to solve customer problems.