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Implement, customize, and operate enterprise AI-driven search and insights solutions. Engage customers through qualification, deployment, and support; design scalable architectures; extend the platform using Python/Bash; integrate LLMs, Elasticsearch, and APIs; manage deployments on Linux and hybrid infrastructures; document solutions and collaborate across teams.

Partner with sales, product, and delivery teams to design, demonstrate, and scope Generative and Agentic AI solutions. Lead technical POVs and demos, translate business needs into scalable architectures, extend platform functionality with Python components (orchestrators, RAG pipelines, prompt frameworks), advise customers on AI adoption, and feed product/engineering with field insights.

Own post-sale success for a portfolio of enterprise clients: drive adoption, run QBRs and renewals, manage end-to-end implementations using agile methods, translate telemetry and GenAI signals into actionable recommendations, and identify expansion opportunities while coordinating cross-functional teams.

As a Knowledge Graph Engineer, you will design and implement knowledge graph solutions, manage client engagements, and collaborate across teams for semantic modeling and customer success.