Cloud AI/ML Data Science Leader

4 days ago

solapur, India beBeeDataScience Full time

Job OverviewWe are seeking a seasoned AI/ML Cloud Engineer to lead the development of our Graph Analytics Platform. As a key member of our team, you will be responsible for architecting and implementing distributed graph computing solutions that process vast amounts of data.Distributed Entity Resolution: Design and implement efficient algorithms to resolve billions of entities and relationships in real-time.Blocking Strategies: Develop optimized strategies using techniques like LSH and canopy clustering to enhance performance at scale.AI/ML Based Matching: Create advanced matching capabilities with explainable AI (XAI) to ensure accurate results.Incremental Resolution: Implement incremental resolution supporting real-time and batch modes to cater to diverse use cases.Entity Lookup APIs: Design APIs for entity lookup with stringent latency requirements, ensuring seamless user experience.Dynamic Entity Resolution: Collaborate with the team to develop dynamic entity resolution with embedded graph knowledge of GNN and GT.Distributed Graph Generation: Architect pipelines using libraries like GraphX/GraphFrames to generate massive graphs.Graph Analytics Algorithms: Implement PageRank, community detection, centrality measures, and other complex graph analytics algorithms.Storage Strategies: Design storage strategies for multi-billion edge graphs in the cloud, optimizing data retrieval and processing.Temporal Graph Support: Build support for time-evolving networks, enabling analysis of dynamic graph structures.High-Performance APIs: Create graph serving APIs with complex query capabilities, ensuring rapid data access and manipulation.Graph Partitioning: Optimize graph partitioning to minimize shuffle and maximize locality, enhancing overall system performance.Graph Neural Networks: Build GNNs and models using neighbor sampling techniques like GraphSAGE to analyze corporate and transaction networks.AI Based Entity Resolution: Implement AI-based entity resolution algorithms using techniques like fuzzy matching, semantic matching, and clustering across heterogeneous data sources.Risk Scoring Models: Create risk scoring models by combining rule-based, supervised, and unsupervised methods, optimized for real-time and large data processing.Model Transparency and Fairness: Champion model transparency and fairness by integrating state-of-the-art evaluation techniques like SHAP, LIME, bias detection, and monitoring model drifts.About UsWe offer a collaborative work environment, opportunities for growth and professional development, and a competitive compensation package. If you're passionate about leveraging AI and ML to drive business value, we encourage you to apply.

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