Distributed Graph Architect

Our company is seeking a Senior Graph Computing Architect to lead the development of our next-generation data intelligence platform.Job Description:The role requires architecting and implementing distributed graph computing solutions that process billions of entities and relationships. The successful candidate will lead the development of the Entity Resolution and Network Generation services.The engineer is expected to be performance-obsessed, think in distributed systems, optimize for latency and throughput, and act as a self-directed technical leader who makes architectural decisions and implements them.This is a foundational role offering the unique opportunity to design, build, and steer a product from its inception, establishing yourself as a key architect in one of the world's leading Graph analytics platforms.ResponsibilitiesEntity Resolution ServiceDesign and implement distributed entity resolution algorithms to process billions of records.Build blocking strategies (e.g., LSH, canopy clustering) optimized for Spark at scale.Create AI/ML based advanced matching with explainable AI (XAI).Implement incremental resolution supporting real-time and batch modes.Design APIs for entity lookup with sub-100ms latency requirements.Assist the team in building dynamic entity resolution with embedded graph knowledge of GNN and GT (Graph transformers).Network Generation ServiceArchitect distributed graph generation pipelines using libraries such as GraphX/GraphFrames.Implement graph analytics algorithms such as PageRank, community detection, and centrality measures.Design storage strategies for multi-billion edge graphs in the cloudBuild 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.Ai/Ml Model Development & Explainable Ai (Xai)Build Graph Neural Networks (GNNs) and models using neighbour sampling techniques such as GraphSAGE to analyse corporate and transaction networks for fraud and risk patterns.Implement AI based Entity Resolution algorithms using techniques such as fuzzy matching, semantic matching (Sentence-BERT), and clustering across heterogeneous data sources.Create Risk Scoring Models by combining rule-based, supervised and unsupervised methods, optimized for real-time and large data processing.Champion model transparency and fairness by integrating state-of-art models evaluation techniques such as SHAP, LIME, bias detection and monitoring model drifts.RequirementsMinimum Qualifications3+ years of experience in distributed computing and big data systems.2+ years' experience in entity resolution and graph analytics at scale.Strong understanding of blocking algorithms, probabilistic record linkage, and similarity measures.Ability to design and optimise large Apache Spark workloads.Ability to implement graph algorithms in distributed computing frameworks such as Apache Spark, Dask or equivalent with optimised queries and storage formatsHave a good understanding of Open Cypher , Gremlin or equivalent.Familiar with Elasticsearch or equivalent for fast search and pattern matching.Understanding of AI/ML applications to entity resolution.Preferred ExperienceMaster or PhD in Computer Science or related field with focus on graphs/entity resolutionExperience with Architecture Design and implementation of large scaled Graph databases (Neo4j, Amazon Neptune, JanusGraph) or equivalentTrack record in building systems processing millions of entities/edgesExperience in designing microservices architectures, fault-tolerant, scalable systems, and API design.Experience with real-time stream processing (Kafka, Flink, Apache Beam, Apache Spark Streaming etc…)Basic experience with Banking compliances (Financial Crime, Fraud)