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:
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Schema Design: Define node labels and edge types that represent the relationships between designers, garments, style attributes, trends, occasions, and editorial articles.
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Entity Ingestion: Build workflows that consume structured JSON payloads from the upstream data pipeline and load them into the graph database in batch mode.
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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.
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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.
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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.
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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.
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Technical Validation: Support technical interviews and validation of new team members joining the graph database workstream, as needed.
Requirements:
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Advanced English proficiency (C1 or above), with autonomy to communicate directly with international stakeholders
- Solid experience with the property graph data model
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Experience writing and optimizing openCypher queries, including multi-hop traversals under latency constraints
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Experience designing graph schemas, evaluating trade-offs between different modeling approaches
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Experience building entity resolution logic, including fuzzy matching, scoring algorithms, and deduplication across data sources
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Strong Python skills for data engineering, including batch processing of structured data payloads
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Ability to work independently in a fast-paced, staff augmentation setting with an established client team
Nice to Have:
- Solid experience with Amazon Neptune
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Prior experience in fashion, retail, or e-commerce catalogs
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AWS certification in databases or data analytics
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Experience integrating graph databases with generative AI agents (for example, Amazon Bedrock Agents)
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Familiarity with recommendation systems or ranking/weighting logic for content surfacing