Customer Success and Engineering Technical Owner
Job Description
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About the Role
As part of the Customer Success and Engineering organization, you will be the primary technical owner of the post-sale relationship for our most strategic accounts. While the Solutions Architect designs the vision, you ensure that vision becomes a reality. You will act as a long-term trusted advisor and the glue between the Customer’s technical team, our Support team, and our Forward Deployed Engineers (FDEs).
You will manage the technical health of the customer, driving alignment across multiple stakeholders to ensure the successful deployment and adoption of our Semantic Search, Agentic AI, and RAG solutions. You are not just managing accounts; you are managing technical success.
What you will own
Orchestrate Technical Delivery: Serve as the primary technical point of contact post-sale, coordinating efforts between Solutions Architects, Forward Deployed Engineers, and Support to ensure seamless project execution.
Drive Adoption & Value: Guide customers through the implementation lifecycle, ensuring they effectively utilize Vector Search to solve their specific real-world problems and achieve their business goals.
Project & Stakeholder Alignment: Manage expectations across multiple customer projects. You will map customer timelines to internal engineering resources and ensure alignment between client stakeholders and our technical teams.
Technical Health Oversight: Conduct regular technical health checks and architecture reviews to identify bottlenecks, suggest optimizations, and maximize usage of the product
Feedback Loop: Act as the strategic voice of the customer. Aggregate technical feedback and friction points from the field to help shape and prioritize the Engineering and Product roadmap.
Crisis Management: Serve as the escalation manager for critical technical issues, mobilizing Support and Engineering resources to resolve blockers in high-stakes deployments.
Evangelize Best Practices: Educate customers on the optimal patterns for building Agentic AI and RAG solutions, moving them from initial use cases to enterprise-wide adoption.
Who you are
5+ years of experience in a customer-facing technical delivery role.
Proven ability to manage complex, multi-stakeholder technical projects and keep them on track without direct authority.
Understanding of the ML lifecycle, data pipelines, and the fundamental concepts of search/retrieval systems.
Ability to translate complex technical blockers into business impact for executives, while also talking shop with developers.
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Qdrant
View Company ProfileQdrant (operating under qdrant.tech, legally Qdrant Berlin GmbH) is the premier, enterprise-grade open-source vector database management system, high-performance neural search engine, and similarity matching orchestration powerhouse engineered to act as the definitive, high-velocity infrastructure layer for Retrieval-Augmented Generation (RAG), large language model (LLM) persistent memory, and multimodal AI applications. Founded in 2021 by expert distributed systems and machine learning technologists Andre Zayarni and Andrey Vasnetsov, the corporate ecosystem completely eliminates the severe performance degradation, index-rebuilding latency spikes, and structural memory erosion commonly associated with legacy relational database plug-ins and primitive K-Nearest Neighbor (KNN) wrappers by building its custom storage architecture entirely from scratch in Rust. Moving far beyond traditional keyword search methods or high-latency post-filtering mechanisms, Qdrant natively unifies expansive JSON metadata payload filtering during HNSW graph traversal, native hybrid search combining dense and sparse vectors, ColBERT-based token-level late interaction reranking, and dynamic multivector storage configurations into a single high-availability data infrastructure framework. As the primary engine trusted by global developer communities and high-scale enterprise platforms, its open-source repository commands over 29,000 GitHub stars and powers critical AI pipelines for forward-thinking organizations globally. Backed by elite international venture groups, the enterprise has secured over $87 million in institutional funding—culminating in a massive Series B capital acceleration led by Spark Capital alongside 42 Capital and AVP to scale its managed Qdrant Cloud operations and native inference engine integrations. Under the hood, its technology stack harnesses advanced vector quantization strategies (including scalar and binary quantization) to execute up to a 64x memory footprint reduction with minimal recall loss, alongside real-time data indexing capabilities that expose newly added vector points instantaneously to active queries. Headquartered in Berlin, Germany, with a globally distributed remote engineering presence, the firm operates with absolute deterministic execution correctness across multi-tenant cloud and on-premise infrastructure environments. What sets Qdrant apart is its uncompromising dedication to replacing complex, multi-stage data orchestration loops with real-time, one-stage filtered similarity traversal and deep memory optimization; by bridging the gap between high-volume programmatic data repositories and millisecond-level neural vector retrieval, the corporation remains a definitive cornerstone of modern infrastructure scaling and worldwide applied artificial intelligence transformation.
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