Principal/Senior Graph Solution Engineer

Requirements :

- Graph and Vector Data Science : Experience in applying graph algorithms, vector embeddings, and data science techniques for enterprise analytics.

- SQL Expertise : Experience in SQL for querying, performance tuning, and debugging in relational and graph contexts.

- Graph Databases and Platforms : Experience with Graph Database Products like Tigergraph, Neo4J, Janusgraph or similar systems, focusing on multi-modal graph + vector integrations.

- Programming & Scripting : Experience in Python, C++, and automation tools for task management, issue resolution, and GSQL development.

- HTTP/REST and APIs : Expertise in building and integrating RESTful services for database interactions.

- Linux and Systems : Strong background in Linux administration, scripting (bash/Python), and distributed environments.

- Kafka and Streaming : Experience with Kafka for real-time data ingestion and event-driven architectures.

- Cloud Computing : Experience with AWS, Azure, or GCP for virtualization, deployments, and hybrid setups.

- Graph Neural Networks (GNNs) and Graph Machine Learning : Hands-on with frameworks like PyTorch Geometric for predictive analytics on graphs.

- Retrieval-Augmented Generation (RAG) and Semantic Search : Building pipelines with vector embeddings and LLMs for AI applications.

- Multimodal Data Handling : Managing text, images, video in graph + vector setups.

- Agile Methodologies and Tools : 3+ years with Scrum/Agile, JIRA, or Confluence.

- Presentation and Technical Communication : Advanced whiteboarding, architecture reviews, and demos.

- Cross-Functional Collaboration : Leading discovery, data modeling (UML, ER diagrams), and on-call incident management.

Nice to have Skills :

- Big Data Processing Tools : Proficiency in Apache Spark, Hadoop, or Flink for distributed workloads.

- AI-Driven Database Management and Optimization : Skills in AI-enhanced query optimization and performance tuning.

- Monitoring & Observability Tools : 4+ years with Prometheus, Grafana, Datadog, or ELK Stack.

- Networking & Load Balancing : Proficient in TCP/IP, load balancers (NGINX, HAProxy), and troubleshooting.

- K8s (Kubernetes) : Proficiency in container orchestration for scalable deployments.

- DevOps and CI/CD Pipelines : Advanced use of Git, Jenkins, or ArgoCD for automation.

- Real-Time Analytics and Streaming Integration : Beyond Kafka, experience with Flink or Pulsar.