Technical Success, EMEA

New
Q
QdrantAI infrastructure
Remote - EMEAFull-TimeMiddle
Salary not disclosed
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Job Details

Experience
3+ years

Requirements

  • 3+ 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.
  • Experience with Kubernetes and Cloud-native ecosystems (AWS, GCP, Azure) (Nice to have).
  • Experience with Search technologies (Elasticsearch, Solr, Pinecone, Milvus, or Qdrant) (Nice to have).
  • Background in working with "Forward Deployed" or "Professional Services" models (Nice to have).

Responsibilities

  • 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.
  • Guide customers through the implementation lifecycle, ensuring they effectively utilize Vector Search to solve their specific real-world problems and achieve their business goals.
  • Manage expectations across multiple customer projects, mapping customer timelines to internal engineering resources and ensuring alignment between client stakeholders and our technical teams.
  • Conduct regular technical health checks and architecture reviews to identify bottlenecks, suggest optimizations, and maximize usage of the product.
  • Act as the strategic voice of the customer, aggregating technical feedback and friction points from the field to help shape and prioritize the Engineering and Product roadmap.
  • Serve as the escalation manager for critical technical issues, mobilizing Support and Engineering resources to resolve blockers in high-stakes deployments.
  • Educate customers on the optimal patterns for building Agentic AI and RAG solutions, moving them from initial use cases to enterprise-wide adoption.
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