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Open nowFirst seen 7 hours ago

Product Solution Architect, Data & AI Solutions - Singapore

ByteDance1,424 open roles

Where
Singapore
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Your applicationOpen nowProduct Solution Architect, Data & AI Solutions - SingaporeByteDance · Singapore

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The clock on this job

Early applications get read.

8.1% of postings close within 7 days. Measured by our own scanner across the market. ByteDance postings stay open a median of 35 days.

Share of postings closed within
  1. 1.7%1 day
  2. 3.6%3 days
  3. 8.1%7 days
  4. 15.1%14 days
  5. 34.0%30 days
This job: first seen 7 hours ago

ByteDance median: 35 days open

The posting

We are seeking a Product Solution Architect with deep technical expertise, industry insight, and a global mindset to join the Data Enterprise Solutions team at Volcano Engine, ByteDance's cloud and AI service platform.

This is a Data + AI scenario-driven, industry-facing product solution role. You will serve as the core technical bridge between the data intelligence product portfolio and global customer business scenarios, translating multimodal data processing operators, large model application capabilities, and enterprise-grade AI agent solutions into industry-ready solutions. This is not a traditional pre-sales role - you will be expected to write code, build demos, run end-to-end POCs, and convince customers with working prototypes.

Responsibilities: - Industry-Focused Data + AI Solution Architecture Design: Lead end-to-end solution architecture design across the full stack, from data infrastructure and multimodal data processing pipelines to enterprise AI agent applications, for customers across industries such as internet, media, finance, automotive, retail, government, and manufacturing. Translate customer pain points into reusable industry solution patterns, and develop standardized solution whitepapers, reference architectures, and best practices. - Key Account Pre-sales and Technical Consulting: Partner closely with sales teams to support strategic accounts through executive-level (CxO) technical engagement, including requirements discovery, solution workshops, architecture reviews, technical deep dives, POC design, and product demonstrations. Independently develop technical proposals, solution design documents, feasibility assessments, and RFP/RFI responses. Deliver clear and fluent English presentations and technical discussions with global customers and stakeholders. - Prototype Development and POC Validation: Serve as the primary technical owner for building and validating prototypes, demos, and proof-of-concepts across multiple AI and data scenarios. Rapidly translate customer requirements into working technical solutions, validate feasibility and business value, and provide actionable recommendations for production rollout. - Multimodal AI and Operator Workflow Scenarios: Design, build, and validate multimodal AI workflows for use cases such as audio/video understanding, intelligent editing, speech-to-text, document parsing, ad creative generation, video translation, video enhancement and restoration, and image generation. Develop a strong understanding of operator capability boundaries, workflow composition, and orchestration logic to support scalable automated content production pipelines. - Enterprise AI Agent Scenarios: Build and validate enterprise AI agent solutions, including RAG-based knowledge assistants, NL2SQL analytics agents, NL2Pipeline data workflow generation, and multi-agent collaboration/orchestration systems. Drive solution design with a strong understanding of enterprise use cases, system integration patterns, and production-readiness requirements. - Data Platform and Analytics Scenarios: Design and implement solutions related to data integration, data governance, BI dashboarding, and growth analytics. Support customers in building structured data foundations, defining event tracking frameworks, and enabling data-driven business insights through scalable analytics solutions. - Technical Delivery and Performance Optimization: Own the full lifecycle of AI application implementation, from model selection and prompt engineering to RAG architecture design, agent development, evaluation, and continuous optimization. Hands-on experience should include embedding optimization, reranking strategy design, hybrid retrieval, vector database selection, and scenario-level performance tuning. Ensure technical solutions can move beyond prototype stage into production deployment and continuously generate measurable business value. - Product Feedback and Ecosystem Collaboration: Capture customer requirements and frontline feedback, and translate them into actionable product and technical input to shape roadmap evolution. Drive improvements in operator capabilities, agent product features, and cross-product integration. Partner closely with product, engineering, and algorithm teams to support strategic benchmark projects and improve overall solution competitiveness. - Industry Knowledge Building and Scalable Replication: Distill project learnings into reusable industry playbooks, SOPs, solution templates, case studies, and best practices to improve repeatability and scale. Enable sales teams and solution architects through training and mentorship, and contribute to market intelligence, objection handling frameworks, and industry-facing solution narratives.

Minimum Qualification(s): - Bachelor’s degree or above in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related technical field. - At least 5 years of experience delivering Data + AI projects across the full lifecycle, from solution discovery and design to implementation and production deployment. - Ability to independently deliver solution presentations, conduct technical discussions, and engage with global customers in cross-regional environments. - Strong understanding of large model technologies, including LLM/VLM fundamentals, mainstream model ecosystems, and model adaptation approaches such as fine-tuning and alignment. - Hands-on experience with AI agent engineering, including RAG architecture, retrieval optimization, vector database selection, and agent workflow orchestration using mainstream frameworks. - Strong understanding of multimodal AI systems, including multimodal data processing pipelines, operator/service orchestration logic, and audio/video/document-related AI application scenarios. - Familiarity with modern data platforms and product stacks, including data infrastructure, data governance, BI/analytics, growth analytics, and data intelligence application scenarios. - Strong engineering execution capability, including the ability to independently build demos, run end-to-end POCs, and support technical troubleshooting in customer-facing scenarios; proficiency in Python or another mainstream programming language is required. - Proven ability to translate customer pain points into structured, reusable industry solutions, with hands-on experience in at least one vertical industry such as media, advertising, e-commerce, finance, retail, automotive, government, or manufacturing.

Preferred Qualification(s) - Prior experience in customer-facing technical roles such as Solution Architect, Pre-sales Technical Expert, Forward Deployed Engineer, or similar positions at cloud providers, AI companies, or leading technology enterprises. - Deep understanding of mainstream foundation models such as Doubao, DeepSeek, Qwen, GPT, Gemini, and Claude, including their relative strengths, limitations, and suitable use cases. - Familiarity with model alignment and optimization approaches such as SFT, LoRA, RLHF, and DPO, with the ability to recommend model strategies based on business needs. - Experience with multi-agent systems, advanced tool orchestration, MCP service development, tool calling, and skills-based interaction design. - Hands-on experience in multimodal AI use cases such as audio/video understanding, intelligent editing, speech-to-text, document parsing, image generation, video generation, video enhancement, or AIGC content production pipelines. - Familiarity with audio/video infrastructure concepts such as codec, transcoding, streaming distribution, RTC, and large-scale multimedia data architecture. - Understanding of data lake / lakehouse architectures and experience managing large-scale unstructured data pipelines. - Experience designing cross-product integrated solutions across data engine, governance, analytics, customer data platform, growth marketing, and enterprise intelligence agent layers. - Strong business abstraction and solution standardization capability, including the ability to distill project learnings into reusable templates, methodologies, and SOPs. - Experience independently leading large-scale projects with contract value in the tens of millions or above. - Experience supporting executive-level (CxO) technical communication, architecture reviews, and strategic pre-sales engagements for key accounts. - Experience in one or more high-priority industries such as short-form video, digital advertising, autonomous driving, embodied intelligence, or other AI-native sectors.

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