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 to have Skills :
• Data Governance, Security, and Compliance : Knowledge of encryption, access controls, GDPR / HIPAA, and ethical AI practices.
• 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.

(ref : hirist.tech)