Distinguished System Architect

3 days ago

Kolkata, West Bengal, India beBeeData Full time US$ 1,20,000 - US$ 1,50,000
Senior Data Intelligence Engineer

The role involves designing and implementing a next-generation data intelligence platform using microservices architecture. We need a senior expert to lead the development of Entity Resolution and Network Generation services.

This is a hands-on technical leadership role where you'll architect and implement distributed graph computing solutions processing billions of entities and relationships. The ideal candidate will have expertise in Apache Spark, Elasticsearch, Scala, and data mesh principles.

Key Responsibilities:

  • Design and implement distributed entity resolution algorithms capable of processing billions of records.
  • Build blocking strategies (e.g., LSH, canopy clustering) optimized for Spark at scale.
  • Develop fuzzy matching algorithms leveraging Elasticsearch's capabilities.
  • Implement incremental resolution supporting real-time and batch modes.
  • Design APIs for entity lookup with sub-100ms latency requirements.

Additional Responsibilities:

  • 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.
  • Optimize graph partitioning to minimize shuffle and maximize locality.

AI Model Development:

  • Develop Graph Neural Networks (GNNs): Build 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.

Cross-Service Responsibilities:

  • Ensure seamless integration between entity resolution and network generation.
  • Design data lineage tracking across both services.
  • Implement comprehensive monitoring and observability.

Requirements:

  • Expertise in Apache Spark, Elasticsearch, Scala, and data mesh principles.
  • Strong understanding of graph theory and graph algorithms.
  • Experience with PyTorch Geometric or DGL.
  • Knowledge of explainable AI techniques.

Preferred Skills:

  • Graph Neural Networks (GNNs)
  • Entity Resolution
  • Risk Scoring Models

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