Advanced Entity Resolution and Network Generation Specialist

1 week ago

Hyderabad, Telangana, India beBeeExpertise Full time US$ 15,00,000 - US$ 30,00,000
Entity Resolution and Network Generation Expert

Our organization is seeking a highly skilled and experienced professional to lead the development of our Entity Resolution and Network Generation services.

  • We are looking for an expert in distributed graph computing solutions who can design and implement large-scale systems, capable of processing billions of records.
  • The successful candidate will have hands-on technical leadership experience in architecting and implementing scalable systems with Elasticsearch and other technologies.

Key Responsibilities:

  • Design and implement distributed entity resolution algorithms leveraging Elasticsearch's capabilities.
  • Develop fuzzy matching algorithms using Elasticsearch's features.
  • Create ML-enhanced matching with explainable AI for match decisions.
  • Implement incremental resolution supporting real-time and batch modes.
  • Design APIs for entity lookup with sub-100ms latency requirements.

Network Generation Service:

  • Architect distributed graph generation pipelines using GraphX/GraphFrames.
  • Implement graph analytics algorithms (PageRank, community detection, centrality measures).
  • Design storage strategies for multi-billion edge graphs in Parquet/distributed file systems.
  • Build temporal graph support for time-evolving networks.
  • Create high-performance graph serving APIs with complex query capabilities.

AI Model Development:

  • Build Graph Neural Networks (GNNs): Develop GNN models (e.g., GraphSAGE, GATv2) using PyTorch Geometric or DGL to analyze corporate and transaction networks.
  • Implement Entity Resolution: Design algorithms for fuzzy matching, semantic matching (Sentence-BERT), and clustering to unify entities across heterogeneous data sources.
  • Create Risk Scoring Models: Combine rule-based, supervised (XGBoost), and unsupervised (Isolation Forest) methods to generate composite risk scores.

Explainable AI (XAI):

  • Champion Transparency: Integrate SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions.
  • Ensure Fairness: Audit models for bias and fairness, embedding ethical principles into every stage of development.

Technical Environment:

  • Languages: Scala (primary), Python, Java.
  • Big Data: Apache Spark 3.x, Hadoop ecosystem.
  • Search: Elasticsearch 8.x.
  • Orchestration: Kubernetes, Docker.
  • Storage: HDFS/S3/GCS, Parquet.
  • Monitoring: Prometheus, Grafana, Jaeger.

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