Chief Graph Computing Specialist

About the Role

We're developing a cutting-edge data intelligence platform using a microservices architecture, and we need a senior expert to lead the development of our 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 Platform

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
• Data mesh principles with API-first design

Core Responsibilities

Entity Resolution Service

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

Network Generation Service

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

AI Model Development

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.

Explainable AI (XAI)

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.

Cross-Service Responsibilities

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 Qualifications

Technical Expertise

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

Domain Knowledge

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

Systems Design

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

Preferred Qualifications

PhD in Computer Science or related field with focus on graphs/entity resolution

Contributions to open-source projects (especially Spark, GraphX, Elasticsearch)

Experience with graph databases (Neo4j, Neptune, JanusGraph) or equivalent

Publications or conference talks on entity resolution or graph analytics

Experience with real-time stream processing (Kafka, Spark Streaming)

Knowledge of graph neural networks and embedding techniques

Technical Environment

Languages: Scala (primary), Python, Java

Big Data: Apache Spark 3.x, Hadoop ecosystem

Search: Elasticsearch 8.x

Orchestration: Kubernetes, Docker

Storage: HDFS/S3/GCS, Parquet

Monitoring: Prometheus, Grafana, Jaeger

CI/CD: Modern DevOps practices

What We're Looking For

Someone who thinks in distributed systems and can optimize for both latency and throughput

A technical leader who can make architectural decisions and implement them

Strong communicator who can explain complex graph concepts to stakeholders

Self-directed engineer who can own large technical initiatives end-to-end

Performance-obsessed developer who benchmarks everything

Impact You'll Make

Define the architecture for entity resolution serving multiple business domains

Build the graph intelligence layer powering advanced analytics and ML

Create systems that will process billions of entities with millisecond latencies

Establish best practices for graph computing in our organization

Mentor other engineers on distributed graph algorithms

Compensation & Benefits

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 up to in excess of USD 150K every year

Interview Process

Technical screen focusing on distributed systems and graph algorithms

System design session on entity resolution at scale

Coding session implementing a graph algorithm in Scala

Architecture discussion with the team

Final round with leadership

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