Sales Engineer

Roles and responsibilities

As a Sales Engineer, you will play a pivotal role in bridging advanced technology with business requirements. Collaborating closely with the Enterprise AI Sales team, your primary focus will be on identifying customer challenges and showcasing the technical strengths of the client's GPU-as-a-Service (GPUaaS) platform. Your efforts will be instrumental in supporting the sales cycle and ensuring successful deployments of AI solutions.

Key Responsibilities:

You will work alongside Enterprise AI Sales representatives to identify and qualify leads that have significant AI workloads. This involves deepening clients' understanding of the GPUaaS solution through technical presentations, engaging demos, and the development of proof-of-concept (POC) projects. A critical aspect of your role will be to translate customer requirements into detailed technical specifications for GPU configurations and compute resources on the platform.

Additionally, you will partner with the sales team to craft compelling proposals that highlight the cost-effectiveness and scalability of the client’s solutions compared to traditional methods. Collaborating with pre-sales engineers, you will configure and implement POC environments to demonstrate the platform's performance and efficiency effectively.

Throughout the sales cycle, you will provide ongoing technical support to the sales team, addressing customer inquiries and concerns promptly. Staying current with the latest advancements in AI, GPU technology, and evolving platform features will be essential to maintaining your expertise and enhancing client interactions.

Desired candidate profile

Qualifications:

The ideal candidate will have a minimum of 3 years of experience in a technical sales role, ideally within the AI or high-performance computing (HPC) sectors. A strong understanding of GPU architectures, particularly the capabilities of models like the A100 and H100, is crucial for accelerating AI workloads. Familiarity with AI frameworks and libraries, such as TensorFlow and PyTorch, will be advantageous.

You should possess excellent communication and presentation skills, with the ability to convey complex technical concepts to non-technical audiences effectively. Building strong relationships with both internal and external stakeholders will be essential, and you should thrive in a collaborative environment. Proficiency in technical documentation and proposal creation is necessary; proficiency in Mandarin is considered a significant asset.

Experience working with AI enterprises to understand their unique needs is important, as is knowledge of containerization technologies like Docker and Kubernetes. Furthermore, hands-on experience developing or managing POC environments for AI projects, specifically in deploying GPU clusters, will be highly valued.