AI Engineering Graduate Talent Trainee

14 days ago

Singapore, SingaporeEntry Level

Responsibilities

Develop large-scale, highly available AI cloud services including GPU virtual machines, bare-metal services, container services, networking, storage, billing, monitoring, security, and multi-region resource management.
Build managed Kubernetes platforms with GPU-native orchestration, intelligent scheduling, workload management, cluster lifecycle management, observability, and resource isolation.
Develop distributed training, model fine-tuning, model deployment, inference acceleration, serverless inference, model APIs, evaluation, and production model-serving capabilities.
Build enterprise AI Agent platforms covering agent workflows, tool integration, retrieval-augmented generation, memory, sandbox execution, multi-agent collaboration, evaluation, observability, security, and runtime infrastructure.
Work on GPU scheduling, distributed systems, high-performance networking, cloud-native infrastructure, MLOps and LLMOps platforms, marketplaces, or automated operations systems.
Participate in AI operations, MLOps, site reliability engineering, AI and cloud security engineering, AI research, or related program areas.
Potentially rotate across different engineering roles based on career development opportunities.

Fresh graduate with a Bachelor's, Master's, or PhD in any discipline, or a candidate with up to two years of related work experience.
Strong programming ability in one or more of Go, Python, C++, Java, Rust, or related technologies.
Experience with Kubernetes, Docker, Helm, Terraform, OpenStack, Prometheus, Grafana, Argo, Istio, PostgreSQL, Redis, message queues, distributed storage, or other cloud-native technologies is highly preferred.
Experience developing or operating large-scale cloud, Kubernetes, AI training, inference, or AI Agent platforms is highly preferred.
Strong enthusiasm for AI infrastructure, cloud-native technologies, and large-scale globally distributed systems.
Demonstrated achievements in academics, engineering projects, top-tier publications, open-source contributions, or programming and algorithm competitions.
Strong ownership mentality, engineering discipline, and commitment to making an early-career impact.

Opportunities to rotate across different roles as part of career development.
Training, mentoring, developmental opportunities, and rapid professional growth.
Inclusive and diverse workplace with open workspaces and a startup-oriented environment.
Opportunities to network with industry pioneers and contribute directly to new projects, processes, and systems.
Personal accountability, autonomy, and the opportunity to work on globally significant AI infrastructure.
Attractive welfare benefits.

201-500 employees