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Senior Distributed Computing Engineer

2 weeks ago

Belgaum, Karnataka, India beBeeData Full time US$ 1,50,000 - US$ 2,00,000

About the Role

">
  • ">
  • 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.">
  • 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.">
  • 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.">
  • Optimize graph partitioning to minimize shuffle and maximize locality.">
  • Build Graph Neural Networks (GNNs): Develop GNN models (e.g., GraphSAGE, GATv2) using PyTorch Geometric or DGL to analyze corporate and transaction networks, detecting fraud rings and risk patterns.">
  • Implement Entity Resolution: Design algorithms for fuzzy matching, semantic matching (Sentence-BERT), and clustering to unify entities across heterogeneous data sources (e.g., CSVs, APIs, PDFs).">
  • Create Risk Scoring Models: Combine rule-based, supervised (XGBoost), and unsupervised (Isolation Forest) methods to generate composite risk scores, optimized for real-time and large data processing in trillions.">
  • Advance Composite AI: Leverage ContexQ's proprietary approach, integrating symbolic AI, vector embeddings, and graph AI for robust entity resolution and network analytics.">
  • Champion Transparency: Integrate SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions, meeting regulatory and ethical standards.">
  • Ensure Fairness: Audit models for bias and fairness, embedding ethical principles into every stage of development.">
  • Ensure seamless integration between entity resolution and network generation.">
  • Design data lineage tracking across both services.">
  • Implement comprehensive monitoring and observability.">
  • Contribute to API design and service contracts.">
  • Optimize for 10x scale growth."]

Required Skills and Qualifications

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  • 7+ years of experience in distributed computing and big data systems.">
  • 5+ years specifically in entity resolution and graph analytics at scale.">
  • Expert-level Scala programming skills.">
  • Deep experience with Apache Spark, including custom optimizations.">
  • Production experience with Elasticsearch for search and matching.">
  • Proven track record building systems processing billions of entities/edges.">
  • Strong understanding of blocking algorithms and their trade-offs.">
  • Experience with probabilistic record linkage and similarity measures.">
  • Expertise in graph algorithms and their distributed implementations.">
  • Knowledge of graph storage formats and query optimization.">
  • Understanding of ML applications in entity resolution.">
  • Basic experience of Banking compliances - FinCrime, Fraud.">
  • Experience designing microservices architectures.">
  • Track record of building fault-tolerant, scalable systems.">
  • API design experience with GraphQL or REST.">
  • Performance optimization and capacity planning expertise."]

Benefits

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  • Competitive senior/staff-level compensation.">
  • Flexible remote work arrangements.">
  • LATEST hardware and cloud resources for development.">
  • LTIP - Long term Incentive plan.">
  • 75% of base as Bonus payment at the end of 4th year in service.">
  • Equity potential of upto in excess of USD 150K every year."]

Others

">
  • ">
  • To apply, please include links to relevant open-source contributions.">
  • Brief description of the largest graph system you've built (nodes/edges scale).">
  • Your approach to a specific entity resolution challenge you've solved.">
  • Any publications or talks on graph computing or entity resolution."]