Data Intelligence Architect

We are seeking an experienced expert to lead the development of a next-generation data intelligence platform.

This is a hands-on technical leadership role where you will architect and implement distributed graph computing solutions processing billions of entities and relationships.

The 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.

Entity Resolution Service

You will 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

You will 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)

You will champion transparency by integrating SHAP, LIME, and GNNExplainer to provide clear, interpretable explanations for model predictions, meeting regulatory and ethical standards.

Ensure fairness by auditing models for bias and embedding ethical principles into every stage of development.

Cross-Service Responsibilities

You will 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

You should have 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

We are 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

You will 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

We offer 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.

Interview Process

The interview process includes a 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, and final round with leadership.

To Apply

Please include links to relevant open-source contributions, brief description of the largest graph system you've built, your approach to a specific entity resolution challenge you've solved, and any publications or talks on graph computing or entity resolution.

We are building something ambitious and need someone who gets excited about processing graphs with billions of nodes and solving entity resolution at unprecedented scale.