Distributed Graph Computing Architect

12 hours ago

Rajkot, Gujarat, India beBeeExpert Full time US$ 2,50,000 - US$ 5,00,000
Job Description

We are seeking a highly skilled Distributed Graph Computing Architect to lead the development of our Entity Resolution and Network Generation services.

This is a hands-on technical leadership role where you will architect and implement distributed graph computing solutions processing billions of entities and relationships. Our cloud-native platform leverages microservices architecture with Kubernetes orchestration, Apache Spark for distributed processing, Elasticsearch for real-time search and fuzzy matching, Scala as the primary development language, and data mesh principles with API-first design.

The successful candidate will be responsible for designing and implementing distributed entity resolution algorithms capable of processing billions of records, building blocking strategies (e.g. LSH, canopy clustering) optimized for Spark at scale, developing fuzzy matching algorithms leveraging Elasticsearch's capabilities, creating ML-enhanced matching with explainable AI for match decisions, and implementing incremental resolution supporting real-time and batch modes.

You will also be responsible for architecting distributed graph generation pipelines using GraphX/GraphFrames, implementing graph analytics algorithms (PageRank, community detection, centrality measures), designing storage strategies for multi-billion edge graphs in Parquet/distributed file systems, building temporal graph support for time-evolving networks, and creating high-performance graph serving APIs with complex query capabilities.

Additionally, you will work on building 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, implementing 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), creating 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, advancing Composite AI: leverage ContexQ's proprietary approach, integrating symbolic AI, vector embeddings, and graph AI for robust entity resolution and network analytics, championing Transparency: integrate SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions, meeting regulatory and ethical standards, ensuring Fairness: audit models for bias and fairness, embedding ethical principles into every stage of development.

A cross-functional team player who can 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

Technical Expertise:

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