Senior Ai/ml Engineer

**About the Role**We're building a next-generation 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 Graph X/Graph Frames Implement graph analytics algorithms (Page Rank, 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., Graph SAGE, GATv2) using Py Torch 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 Contex Q'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 Expertise7+ years of experience in distributed computing and big data systems5+ 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 - Fin Crime, Fraud**Systems Design**Experience designing microservices architectures Track record of building fault-tolerant, scalable systems API design experience with Graph QL or RESTPerformance optimization and capacity planning expertise**Preferred Qualifications**Ph D in Computer Science or related field with focus on graphs/entity resolution Contributions to open-source projects (especially Spark, Graph X, Elasticsearch)Experience with graph databases (Neo4j, Neptune, Janus Graph) 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 Dev Ops 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 MLCreate 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 upto INR 1.5 CR+ 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 We're building something ambitious and need someone who gets excited about processing graphs with billions of nodes and solving entity resolution at unprecedented scale.

If you've been looking for a role where you can push the boundaries of what's possible with distributed graph computing, we want to talk to you.

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