Business Development Manager - Public Sector Team

Responsibilities

1. Customer Relationship Building and Opportunity Development

Proactively analyze public sector industries in Hong Kong, identify key/potential customers-particularly those with emerging AI/ML workloads, GPU computing needs, or model inference requirements-and effectively map out the decision-making chain to understand their true needs.

Communicate with customers in their language, build trust, uncover product and technical requirements across cloud infrastructure, AI platform, and model serving domains, and generate qualified sales opportunities.

Develop existing customer accounts further by identifying upsell opportunities in AI/intelligent computing and model serving scenarios, enhancing their experience through improved service, business collaboration, and other means to increase resource utilization.

Identify the customer's decision-making chain-including AI/data science leadership-cultivate relationships with key decision-makers, establish long-term business and technical relationships with C-level executives (CXO) or their direct reports (CXO-D), and secure their crucial support throughout the collaboration process.

2. Product and Technology Value Communication

effectively communicate the technical value of Alibaba Cloud products such as Apsara Stack, the "Seven Core Products," Model Studio, GPU/intelligent computing clusters, and Model Serving solutions (including model inference acceleration, elastic scaling, and MLOps capabilities). Positioning Alibaba Cloud as the customer's ideal partner for both cloud infrastructure and AI transformation.

Independently present Alibaba Cloud's standard product solutions-including AI model training, fine-tuning, and serving architectures-to customers. With the support of Solution Architects (SAs), independently present customized solutions tailored to specific customer business scenarios (e.g., large language model deployment, real-time inference pipelines, AI-powered public services)

3. Driving Decision-Making and Opportunity Conversion

Employ a range of sales strategies, including persuasive communication, competitive pricing, and AI proof-of-concept demonstrations, to convert opportunities into closed deals, achieving mutually beneficial outcomes for both Alibaba Cloud and the customer.

Identify and escalate customer experience issues encountered during product usage-especially those related to model serving performance, GPU resource availability, and AI platform usability

4. Business Support

Provide comprehensive business support throughout the customer lifecycle, integrating resources (including AI engineering, model optimization, and intelligent computing specialists) to ensure project success and promptly identifying and resolving product usage issues.

Facilitate the rapid iteration of Alibaba Cloud products within the customer environment, supporting the achievement of their technical objectives across traditional cloud workloads and AI/model serving scenarios

Requirements

• Over 8 years of experience in the cloud computing industry, with experience in public cloud sales, and To B/G sales and pre-sales related work. Familiar with AI infrastructure offerings including GPU instances, model serving platforms, and MLOps toolchains.

• Familiar with top-tier clients in the region, with experience collaborating with or working for them on cloud-native and AI/ML workload migrations.

• Independently calls high-level clients (CEOs/CTOs/business leaders), engaging in effective business and technical communication-including articulating the value of generative AI, large model inference, and model-as-a-service offerings-to influence and accelerate decision-making.

• Guides and assists team members in project negotiations, leads partner discussions (including ISV and model ecosystem partners), and influences negotiation outcomes through the decision-making chain.

• Based on insights into specific market segments, guides customer needs, conducts comprehensive reviews and diagnoses of customer strategies, businesses, and technologies, and provides forward-looking solutions incorporating AI adoption roadmaps, model serving architectures, and inference cost-optimization strategies.

• Understands mainstream architecture design methods-including model serving stacks (e.g., vLLM, TensorRT-LLM, Triton Inference Server), GPU cluster scheduling, and scalable inference endpoint design-and can answer partners' and customers' general questions about technical architecture.

• Identifies deep-seated issues in customer service and influences customer business decisions, particularly around build-vs-buy choices for AI capabilities, model fine-tuning vs. prompt engineering trade-offs, and TCO analysis for self-hosted vs. managed model serving.

• Leads in creating a communicative atmosphere, grasps others' key communication points, listens effectively, and expresses viewpoints clearly across both traditional cloud and emerging AI/LLM topics.

• Possesses strong service awareness, anticipates customer needs around AI transformation, and has deeper expertise than the customer in areas such as model deployment best practices, inference performance tuning, and responsible AI governance.