售前岗位招聘信息20260822

【工作职责】：

1. 客户需求与售前方案

面向汽车、3C、高端装备、物流仓储等制造业客户，深入理解业务场景、预算及决策链，挖掘产线优化、工艺仿真、数字孪生、机器人仿真等需求，并转化为可落地的商业机会。

主导客户调研、方案设计、技术交流、投标及方案宣讲，支持销售提升项目赢单率。

2. 项目全流程闭环

负责从需求定义、方案设计到项目交付、验收的全过程把控，确保售前承诺与实际交付一致；识别关键风险并推动解决，同时挖掘客户复购及增购机会。

3. 具身智能 × 工业仿真方案探索

结合机器人本体、具身模型及仿真能力，探索「仿真场景 + 数据生产 + 模型训练/评测 + 真机部署」的一体化解决方案，并沉淀可复用的行业方案、场景库及最佳实践。

4. 行业洞察与产品反馈

持续跟踪工业仿真、机器人及具身智能行业趋势，将客户需求和一线场景转化为产品及技术需求，支持产品规划、市场活动及生态伙伴联合拓展。

【任职要求】：

2–8 年数字工厂、工业仿真、智能制造相关售前、解决方案或咨询经验；

西门子、施耐德、埃森哲、IBM 等数字化/工业咨询背景优先，机器人集成商背景亦可；

熟悉制造业核心场景，包括工艺规划、产线设计、物流规划、机器人集成等；

熟悉主流工业仿真工具及方法，能够独立完成客户调研、需求分析和方案设计；

具备较强的方案撰写、PPT及客户沟通能力，能够同时与管理层和技术团队沟通；

对具身智能/机器人有兴趣，了解 VLA、强化学习、Isaac Sim、MuJoCo 等技术者优先；

英语可作为工作语言者优先。

【岗位职责】 

AI全栈算力调度大模型底座LLMAgent 智能体模型部署

岗位核心画像

定位：产研侧 AI 全栈解决方案架构师，不是纯售前架构，也不是纯后端开发；属于产研内部的技术‑业务‑商业化桥梁，对内做 AI 全栈体系顶层规划、战略拆解、产品需求转化；对外给商业化 / 售前做技术兜底、重点项目技术支撑。

核心画像拆解

1. 能力底座：懂 AI 全栈完整技术栈

熟悉算力调度、推理引擎、云底座、数据平台、大模型、智能体 Agent、行业场景应用一整套体系；不要求深度开发，但要懂各模块原理、边界、约束、集成关系，能做整体方案架构设计、可行性评估。

2. 双视角能力：产研视角 + 商业化视角

• 对内：承接公司 AI 战略，拆解成产品迭代、技术方案、能力建设，对齐内部产研各团队，做体系化沉淀、标准化方案建设；

• 对外：听得懂客户业务诉求，甄别市场需求，把客户需求翻译成产研可落地需求；支撑售前答辩、标书、重点项目 FDE 落地，能做项目技术兜底。

3. 产出物导向：输出解决方案体系、架构蓝图、标准化方案模板、落地路径、需求规格，而不是写代码

擅长方案设计、技术评审、跨团队对齐、项目卡点推动闭环、案例复盘沉淀。

4. 经验背景分层

• 最优：大厂 AI 产研内部解决方案架构 / PDSA / 解决方案架构，做过大模型 / 智能体全栈方案体系建设，既做内部产研体系规划，又参与重点商业化项目交付支撑；

• 次优：AI 解决方案架构师，有产研背景（不是纯销售售前），经历过大模型平台、Agent、算力推理平台类项目；

5. 软素质

战略拆解能力、跨多团队协同推动、方案宣讲答辩、技术风险识别，既能跟研发聊技术底座，也能跟业务 / 客户聊业务场景与商业化落地。

【岗位职责】:

1. Overseas Big Data & AI Market Insight and Competitive Analysis

• Identify market opportunities, assess market capacity, and analyze the competitive landscape for Big Data and AI products in overseas markets.

• Analyze core metrics, market strategies, and pricing models of international competitors.

• Rapidly capture emerging market trends and customer pain points to uncover business opportunities, accelerate solution deployment, and establish a leading competitive edge.

2. International Business Opportunity Assessment and Deep Technical Engagement for Big Data & AI Products

• Represent the product line in designing international business strategies and evaluating commercial opportunities for Big Data and AI products.

• For complex project requirements, collaborate with sales teams to conduct deep technical exchanges with customers. Leverage insights into industry trends and technological shifts to guide key decision-makers in specific technical scenarios, thereby facilitating opportunity conversion.

• Conduct in-depth technical discussions and present product solutions to international clients and English-speaking stakeholders.

3. Solution Design and Technical Support for Big Data & AI Products

• For complex projects, thoroughly understand customer business needs, functional/non-functional requirements, performance, and availability standards. Based on specific customer scenarios, deliver technically competitive, feasible, and cost-effective product combination solutions. Provide technical support during product selection, Proof of Concept (PoC), and quotation configuration.

• Extract key technical indicators based on customer business scenarios to formulate leading control criteria, validating them through PoCs, win-back initiatives, and other business activities.

• Drive cross-team collaboration to optimize solutions for complex projects, enhancing competitiveness across multiple dimensions including cost, performance, and stability.

• Explore solutions and scenarios for innovative products to accelerate market coverage and ensure sustained product innovation vitality.

• Provide critical post-sales technical Q&A for key accounts, leveraging technology to drive business growth.

4. Product Design and Optimization Support for Big Data & AI Products

• Leverage deep understanding of industries and scenarios to participate in the design of major product features, pricing strategies, and user experiences, ensuring the product maintains leadership within specific sectors and use cases.

• Identify and precisely distill common customer needs and pain points to feed back into product design, driving product improvements, multi-product integration, and the incubation of new products and features.

5. Best Practice Accumulation and Enablement

• Consolidate best practices for niche scenarios; selectively output Infrastructure as Code (IaC) scripts. Summarize benchmark success stories from project implementations to extract common modules and standardize capabilities, accelerating the scaled replication of product solutions.

• Highlight product features and performance parameters, designing targeted test cases to amplify product and technical influence. Build solution competitiveness grounded in robust test cases and testing methodologies.

• Contribute to the development of Go-to-Market (GTM) materials for Big Data and AI products, co-create joint solutions with partners, and provide enablement training for sales teams and ecosystem partners.

【职位要求】：

• Education: Bachelor's degree or higher in Computer Science, Software Engineering, or a related field.

• English Proficiency: Strong written and verbal English skills, capable of conducting in-depth technical exchanges and product solution discussions with international English-speaking clients.

• Experience: 5+ years of experience in the Big Data & AI domain, including at least 2 years in roles such as Solution Architect, Technical Consultant, or transitioning from a core R&D position.

• Distributed Computing Frameworks: Deep understanding of the principles, tuning, and applicable scenarios of core components including Hadoop, Spark, Flink, Presto/Trino, and Kafka.

• Data Architecture Evolution: Profound understanding and hands-on experience with Lambda architecture, Kappa architecture, and Data Lakehouse architecture, knowledgeable about compute-storage separation architecture design and cloud-based Big Data services (e.g., EMR, MaxCompute, Redshift).

• Data Storage & Format: Familiarity with object storage systems like HDFS, S3, and OSS, expertise in open table formats such as Parquet, ORC, Delta Lake, Iceberg, and Hudi.

• AI Infrastructure Hardware: Solid foundation in AI Infra hardware, including heterogeneous computing server architectures, high-performance networking, GPU/AI chips and interconnects, CPU/GPU/Memory/Storage access patterns, server networking topologies, multi-level storage system architectures, and their capability boundaries.

• AI Practical Skills: Hands-on capabilities in Python/Shell programming, invoking Large Language Model (LLM) SDKs, and utilizing mainstream open-source frameworks, product SDKs, or standardized services for ML/DL model inference, training, and deployment.

• Data Governance & Security: Familiarity with data lineage, metadata management, data quality monitoring, and privacy-preserving computation technologies.

• Business Acumen: Ability to comprehend customer business objectives (cost reduction, efficiency improvement, compliance, innovation) and design technical solutions accordingly. Candidates with proven records in building enterprise Big Data platforms and migrating on-premise data centers to Cloud are strongly preferred.

• Candidates must possess a professional customer service mindset, strong teamwork capabilities, the ability to translate complex technical concepts into clear business value for clients, and a commitment to excellence. They are expected to thrive in dynamic environments by actively facing and embracing change.

【职位描述】

1.深入分析客户业务场景与技术需求，基于腾讯云全栈产品与综合优势，构建具有竞争力的行业解决方案，并推动内部产品持续优化与演进；

2.协同销售团队拓展与维护企业客户，为客户的全球化业务布局提供架构咨询，支持客户上云旅程，主导各类技术方案的设计、输出，并推动实施与持续优化；

3.与产业链上下游伙伴及合作伙伴共建行业生态，推动行业数字化发展。

【岗位要求】

1.本科及以上学历，5年以上云计算行业售前经验，具备研发或运维背景者优先；

2.技术基础扎实，在云计算、大数据、AI、物联网、安全等至少一个领域，拥有主流云厂商的实践经验；具备跨云迁移经验者优先；

3.对消费电子、制造、能源等一个或多个行业有深入洞察，理解其商业模式、业务场景及上云需求；

4.熟悉全球主流的安全与合规标准体系，并具备海外数据合规（如数据本地化、合规审计）的实战经验；

5.具备出色的沟通与客户影响力，同时掌握业务、技术语言，能与决策者、技术人员有效对话，精准理解与分析客户需求，并能够高质量地编写解决方案与文档；

6.拥有强烈的团队协作精神、优秀的跨部门推动能力、快速学习能力、自驱力与抗压能力；

7.英语听说读写流利，有海外工作/生活经历或海外客户支持经验者优先。