Senior Customer Architect (Search)

What is the Role

We are actively seeking a Customer Architect who specializes in Enterprise & Vector Search technologies. As a Customer Architect, you will be the Trusted Technical Advisor, working in tandem with the customer and complementary groups like Field and Services Teams to ensure that Elastic solutions are exceeding customer’s expectations. Your technical expertise, eye for business, and ability to deliver value will accelerate and improve the customer experience leading to customer continued satisfaction and expansion within the elastic portfolio.

In your role, a crucial part of your responsibility is defining and communicating the technical success criteria for our customers. This involves crafting detailed Technical Success Plans tailored to the specific customers goals and critical metrics.

The Technical Success Plans act as a roadmap, outlining the optimal consumption and deployment of Elastic solutions in the customer's environment. They detail the necessary steps for implementation, highlight achievements for successful consumption or expansion, and provide and effectiveness aligned to the customer’s business goals. By actioning this plan , you will be seen as the trusted advisor, guiding the customer towards achieving their desired outcomes, thereby ensuring their success and satisfaction.

From providing technical guidance and sharing standard methodologies to aligning technical objectives with the customers' business goals, your work is instrumental in facilitating smooth adoption, sustained usage, and increased consumption of our solutions. This hands-on role requires you to be proactive, with a keen understanding of our customers' technical landscape and a commitment to making their journey with Elastic solutions successful and valuable.

You will be the critical link between Elastic’s GenAI Search innovations and our enterprise customers' success. By delivering technical excellence across indexing, vector embeddings, relevance tuning, and RAG architectures, you’ll drive adoption, build trust, and unlock new ARR through advanced search use cases—empowering semantic retrieval, intelligent recommendations, and knowledge discovery.

You’ll bring deep hands-on expertise in designing, deploying, and optimizing search platforms—ranging from traditional Elasticsearch-based Enterprise Search to modern semantic and vector search architectures (e.g., embeddings, RAG, hybrid search systems). You should be comfortable operating in cloud-native environments (AWS/Azure/GCP), implementing robust indexing pipelines, tuning search relevance, and scaling for performance and cost-efficiency.

• Architect and implement advanced Enterprise Search, Vector Search, and Semantic/RAG systems for large-scale deployments.
• Develop and implement Technical Success Plans tailored to each customer, covering indexing pipelines, query relevance (e.g., recall@k, latency), vector similarity performance, and adoption metrics.
• Lead the full customer lifecycle: onboarding, proof-of-concept (POC), rollout, adoption, escalation, and expansion.
• Design RAG and semantic search workflows using embedding models (LLM, ELSER, Hugging Face, LangChain, etc.).
• Optimize search relevance and performance: tuning indices, scaling clusters, running vectors, and conducting A/B tests.
• Partner with Sales, Pre‑Sales, Services, and Support to align technical strategy and customer satisfaction.
• Monitor usage, diagnose adoption gaps, and proactively propose expansions and upsells aligned with ARR goals.

• 8 – 12+ years building and operating enterprise search systems, including vector and semantic search.
• Strong experience with Elastic or similar platforms: Elasticsearch, Pinecone, Qdrant, Milvus, Weaviate.
• Authority in embedding‑based retrieval, LLM integration, RAG pipelines, and vector database architecture.
• Consistent track record of designing scalable, performant search infra on cloud platforms (AWS/Azure/GCP).
• Excellent communicator able to present technical designs and business impact to both technical and executive audiences.
• Data-driven mentality: adept at measuring search performance (e.g., latency, throughput, recall) and acting on insights.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Previous roles in Solution Architecture, Pre‑Sales Engineering, or Customer Success focused on search/AI applications.
• Nice to Have:
• • Hands-on experience with RAG systems, chat/search applications, and open-source contributions.
  • Understanding of vector database internals: ANN algorithms, indexing strategies, recall/precision evaluation.
  • Familiarity with embedding model tuning and search relevance optimization frameworks.