Software Development Engineer 3 - Graph Engineering

Adobe Systems
Noida, Bengaluru
Workplace: OnsiteFull timeFunction: Product ManagementExperience: 6+ yearsEducation: bachelorsSkills: ["Cross-functional collaboration","Experimentation"]

Build and evolve Adobe’s unified fraud detection graph by merging multiple fraud and account data sources into a scalable, rebuildable model. Develop large-scale ingestion and feature pipelines on Databricks and Spark, apply Graph Data Science algorithms (community detection, centrality, embeddings), and implement graph ML/GNN models to extract risk signals. Translate prototypes into production systems with query optimization and operational health, partnering across data science, product, and platform teams.

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FursaFursa
Adobe Systems
Adobe Systems
1 day ago

Software Development Engineer 3 - Graph Engineering

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Source: Company careers pageValidated by: Fursa AI
Last checked: 8 hours agoStatus: Live

Job Summary

Build and evolve Adobe’s unified fraud detection graph by merging multiple fraud and account data sources into a scalable, rebuildable model. Develop large-scale ingestion and feature pipelines on Databricks and Spark, apply Graph Data Science algorithms (community detection, centrality, embeddings), and implement graph ML/GNN models to extract risk signals. Translate prototypes into production systems with query optimization and operational health, partnering across data science, product, and platform teams.
Location: Noida, Bengaluru
Workplace: Onsite
Employment Type: Full time
Job Function: Product Management
Seniority: Mid level

Key Responsibilities

  • •Build and evolve a unified graph schema merging multiple fraud and account data sources into a consistent, rebuildable model.
  • •Develop and enhance graph ingestion and feature pipelines on Databricks and Spark to transform raw events into graph nodes, edges, and properties.
  • •Apply Graph Data Science algorithms (community detection, centrality, embeddings) to identify abuse rings and coordinated fraud signals.
  • •Develop graph machine learning models (including GNNs) to extract high-value risk signals and integrate them into downstream ML workflows.
  • •Translate prototypes into production graph systems, ensuring scalability, reliability, observability, and operational health (incremental refresh, monitoring, and cost efficiency).

Key Requirements

  • •6+ years building and deploying data or ML solutions at scale.
  • •Bachelor’s (or graduate degree) in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience).
  • •Strong Python skills and practical experience developing large-scale pipelines on Databricks and Spark.
  • •Experience with graph platforms (e.g., Neo4j, Amazon Neptune, TigerGraph, Memgraph) and Graph Data Science (GDS).
  • •Experience applying GDS algorithms such as PageRank, Louvain/Label Propagation, or Node Embeddings (FastRP, Node2Vec).
Experience:6+ years
Education:Bachelor's
Skills:Cross-functional collaborationExperimentation
Tech Stack:PythonDatabricksSparkGraph Data ScienceNeo4jAmazon NeptuneTigerGraphMemgraphNeo4j AuraWCCLouvainLabel PropagationPageRankFastRPNode2VecGraph Neural NetworksGNNsCI/CDMLOps

Company Brief

Adobe Systems
Provides creative, marketing, and document management software and cloud services, including Photoshop, Illustrator, Acrobat, and the Adobe Experience Cloud, serving creative professionals, enterprises, and governments worldwide.
Industry: SaaS
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Jose, United States
Founded: 1982
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