Graph Architect

7 days ago

kochi, India beBeeExplainability Full time

As a highly skilled Senior AI/ML professional, you will play a pivotal role in driving the development of our Graph Analytics Platform.This is an exciting opportunity to tackle complex technical challenges in distributed graph computing, advanced analytics, and optimization.You will lead the design and implementation of Entity Resolution and Network Generation services, leveraging your expertise in distributed algorithms and large-scale data processing.The successful candidate will have strong understanding of blocking algorithms, probabilistic record linkage, and similarity measures, as well as experience with Architecture Design and implementation of large scaled Graph databases.Main Responsibilities:Entity Resolution Service:Design and implement distributed entity resolution algorithms to process vast amounts of dataBuild blocking strategies optimized for scalability and performanceCreate advanced matching solutions with explainable AI (XAI)Implement incremental resolution supporting real-time and batch modesDesign APIs for entity lookup with sub-100ms latency requirementsAssist the team in building dynamic entity resolution with embedded graph knowledge of GNN and GTNetwork Generation Service:Architect distributed graph generation pipelines using libraries such as GraphX/GraphFramesImplement graph analytics algorithms such as PageRank, community detection, and centrality measuresDesign storage strategies for multi-billion edge graphs in the cloudBuild temporal graph support for time-evolving networksCreate high-performance graph serving APIs with complex query capabilitiesOptimize graph partitioning to minimize shuffle and maximize localityAI/ML Model Development & Explainable AI (XAI):Develop Graph Neural Networks (GNNs) and models using neighbour sampling techniques such as GraphSAGEImplement AI based Entity Resolution algorithms using techniques such as fuzzy matching, semantic matching (Sentence-BERT), and clustering across heterogeneous data sourcesCreate Risk Scoring Models by combining rule-based, supervised and unsupervised methods, optimized for real-time and large data processingChampion model transparency and fairness by integrating state-of-art models evaluation techniques such as SHAP, LIME, bias detection and monitoring model drifts

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