[Job - 30900] Master Data Developer (Amazon Neptune), Brazil
在 CI&T,我们帮助大型企业将人工智能的潜力转化为真正的商业影响。AI 部署，AI 原生执行和技术集成商业解决方案。在技术转型方面30年的经验，我们通过 Agentic SDLC,应用现代化，数据与 AI,Martech 和商业战略方面的专业知识加速创新。我们有超过25个国家的8,000名 CI&T 员工，共同构建具有实际影响力的解决方案。人工智能已经是我们每天工作，发展和创新的组成部分。随着 CI&T 发展其数据和分析卓越中心，我们寻求一个有才华和经验的图形数据库开发人员加入专业团队构建 AI 驱动时尚咨询平台，为时装和零售行业的领先客户。这个角色是转化非结构化的编辑内容成为结构化的知识图表的核心，以实时提供支持参加正式活动的最终用户智能风格建议。图形数据 base 开发者将在员工增强模式下工作，与包括机器学习开发商和 GenAI 代理开发人员的跨功能团队集成。这是一个实践技术职位，需要对图形数据层的强有力的拥有。从方案设计到摄入，实体分辨率和查询性能，直接影响 AI 代理向用户推的内容。责任：· 计划设计：定义设计者，服装，风格属性，趋势，场合和编辑文章之间的关系的节点标签和边缘类型。·实体摄入：构建使用上游数据管道结构化的 JSON 有效载荷并按批量模式将其加载到图形数据库中的工作流程。· 可能实体匹配：匹配提取的项目提到 （例如，按类型，颜色和材料描述的一包）在50,000+ SKU 目录中正确产品节点，通过分数属性重叠，分配匹配信心，并只在超过定义门时创建关系。不一致地出现不同的文章中的项目的减倍权：根据一个项目或趋势出现在多个位置，将重量赋予关系，区别于一个专用特征与过去的提及。这些重量决定 AI 代理在其建议中首先表现的是什么。·查询优化：设计和调整开启 Cypher 穿越查询，包括多跳查询，以返回数据
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At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
As CI&T grows its Data & Analytics Center of Excellence, we seek a talented and experienced Graph Database Developer to join a specialized team building an AI-powered fashion advisory platform for a leading client in the fashion and retail industry. This role is central to turning unstructured editorial content into a structured knowledge graph that powers real-time, intelligent style recommendations for end users attending formal events.
The Graph Database Developer will work in a staff augmentation model, integrating with a cross-functional team that includes a Machine Learning Developer and a GenAI Agent Developer. This is a hands-on technical position requiring strong ownership of the graph data layer — from schema design through ingestion, entity resolution, and query performance — with direct impact on what the AI agent recommends to users.
Responsibilities:
- Schema Design: Define node labels and edge types that represent the relationships between designers, garments, style attributes, trends, occasions, and editorial articles.
- Entity Ingestion: Build workflows that consume structured JSON payloads from the upstream data pipeline and load them into the graph database in batch mode.
- Probabilistic Entity Matching: Match extracted item mentions (for example, a bag described by type, color, and material) to the correct product node in a catalog of 50,000+ SKUs by scoring attribute overlap, assigning a match confidence, and creating the relationship only when it exceeds a defined threshold. Deduplicate items that appear inconsistently across different articles.
- Editorial Signal Weighting: Assign weights to relationships based on how prominently an item or trend was featured, distinguishing a dedicated feature from a passing mention. These weights determine what the AI agent surfaces first in its recommendations.
- Query Optimization: Design and tune openCypher traversal queries, including multi-hop queries, to return relevant results in under three seconds for downstream Amazon Bedrock Agents.
- Cross-Functional Collaboration: Work alongside the Machine Learning Developer and GenAI Agent Developer to align the graph structure with the retrieval and recommendation needs of the AI agent.
- Technical Validation: Support technical interviews and validation of new team members joining the graph database workstream, as needed.
Requirements:
- Advanced English proficiency (C1 or above), with autonomy to communicate directly with international stakeholders
- Solid experience with Amazon Neptune and the property graph data model
- Experience writing and optimizing openCypher queries, including multi-hop traversals under latency constraints
- Experience designing graph schemas, evaluating trade-offs between different modeling approaches
- Experience building entity resolution logic, including fuzzy matching, scoring algorithms, and deduplication across data sources
- Strong Python skills for data engineering, including batch processing of structured data payloads
- Ability to work independently in a fast-paced, staff augmentation setting with an established client team
Nice to Have:
- Prior experience in fashion, retail, or e-commerce catalogs
- AWS certification in databases or data analytics
- Experience integrating graph databases with generative AI agents (for example, Amazon Bedrock Agents)
- Familiarity with recommendation systems or ranking/weighting logic for content surfacing
Our benefits:
-Health and dental insurance
-Meal and food allowance
-Childcare assistance
-Extended paternity leave
-Partnership with gyms and health and wellness professionals via Wellhub (Gympass) TotalPass;
-Profit Sharing and Results Participation (PLR);
-Life insurance
-Continuous learning platform (CI&T University);
-Discount club
-Free online platform dedicated to physical, mental, and overall well-being
-Pregnancy and responsible parenting course
-Partnerships with online learning platforms
-Language learning platform
And many more!
More details about our benefits here:
At CI&T, inclusion starts at the first contact. If you are a person with a disability, it is important to present your assessment during the selection process. See which data needs to be included in the report by clicking here.This way, we can ensure the support and accommodations that you deserve. If you do not yet have the assessment, don't worry: we can support you in obtaining it.
We have a dedicated Health and Well-being team, inclusion specialists, and affinity groups who will be with you at every stage. Count on us to make this journey side by side.
Originally posted on Himalayas