商汤AI LLM Development Lead

• Lead the architecture, design, and development of intelligent agent systems that integrate LLMs with real-world applications. • Drive full-stack engineering implementation, including backend services, API integration, database design, and task orchestration. • Select and optimize system components such as message queues, middleware, vector databases, and caching frameworks to meet performance and scalability targets. • Work closely with product and research teams to translate AI agent logic (e.g., tool-use, planning, reasoning) into robust, production-grade systems. • Take ownership of system performance tuning, including concurrency handling, throughput optimization, and service reliability. • Guide the team through best practices in code quality, CI/CD pipelines, and system observability. • Build and lead a team of engineers to deliver high-quality agent-driven applications from prototype to deployment.

• Lead technical exploration with customer architects to understand models, frameworks, SLOs, and KV cache usage patterns. • Build end-to-end KV cache solutions using tiered memory and NVIDIA modern networking technologies. • Analyze performance profiles, identify bottlenecks, and drive PoCs and benchmarks to validate improvements. • Translate customer difficulties into clear feature requests and roadmap input for NVIDIA products. • Build reference architectures, best-practice guides, and deliver tech talks to support our field teams and customers.

1, Market Strategy & Business Expansion: Design and practice market expansion for AI LLM products (e.g., Qwen MaaS platforms, API services, on-premise deployment solutions) across target industries such as FSI, DNB, gaming, e-commerce, and government in South Pacific Region, including SEA markets such as Indonesia, Thailand, Philippines and Vietnam AI markets, etc. Develop and execute sales strategies for assigned regions or verticals to achieve revenue and token targets. 2, Solution Selling & Deal Execution: Deeply understand client business pain points; collaborate with pre-sales/solutions teams to translate LLM capabilities into tangible business value (e.g., cost reduction, efficiency gains, experience enhancement, innovation). Own end-to-end commercial processes: business negotiation, RFP/RFQ responses, contract closure, and payment collection; drive POC (Proof of Concept) conversion rates. 3, Ecosystem & Partnership Development: Build and maintain strategic partnerships with LLM providers, ISVs (Independent Software Vendors), system integrators, and industry consultants to create a robust LLM adoption ecosystem. Co-develop joint solutions and leverage partner channels for scalable customer acquisition. 4, Key Account Management & Customer Success: Nurture long-term relationships with strategic accounts; identify up-sell and cross-sell opportunities. Capture frontline market feedback and customer insights to inform product roadmap and drive iterative improvements of LLM offerings. 5, Market Intelligence: Monitor industry trends, competitor strategies, and regulatory developments in the generative AI/LLM landscape; deliver actionable market analysis to support leadership decision-making.

• Drive business impact across multiple feature areas by defining success metrics, identifying long-term investment opportunities, and aligning with Microsoft’s AI strategy. • Own the roadmap for intelligent collaboration experiences, including Copilot integrations, and lead scenario walkthroughs and golden configuration development with partner teams. • Define and deliver feature sets that balance AI innovation with fundamentals like sync reliability, performance, and accessibility; facilitate usability reviews with customers and stakeholders. • Leverage AI tools and agents to accelerate product development workflows, improve decision-making, and model best practices as an AI-native PM. • Partner with engineering, design, and marketing to build and launch features at scale, including rollout plans, customer support strategies, and internal enablement. • Engage with customer communities, MVPs, and enterprise stakeholders to build trust, gather feedback, and identify opportunities for deprecation or iteration. • Collect performance metrics and form hypotheses to improve product quality and relevance, especially in areas with complex technical or compliance requirements.