Senior Customer Architect - Search

About the Role

Key Responsibilities

• Be the critical link between Elastic's GenAI Search innovations and enterprise customer success.
• Deliver technical excellence across indexing, vector embeddings, relevance tuning, and RAG architectures to drive adoption and build trust.
• Design, deploy, and optimize search platforms ranging from traditional Elasticsearch-based Enterprise Search to modern semantic and vector search architectures (e.g., embeddings, RAG, hybrid search systems).
• Architect and implement sophisticated 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, vector similarity performance, and adoption metrics.
• Own 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 by tuning indices, scaling clusters, running vectors, and conducting A/B tests.
• Partner with Sales, PreSales, 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.

Requirements

• 8+ years building and operating enterprise search systems, including vector and semantic search architectures.
• Deep hands-on expertise in designing, deploying, and optimizing search platforms.
• Experience operating in cloud-native environments (AWS/Azure/GCP).
• Knowledge of implementing robust indexing pipelines, tuning search relevance, and scaling for performance and cost-efficiency.
• Ability to design and implement RAG and semantic search workflows using embedding models (LLM, ELSER, Hugging Face, LangChain, etc.).
• Strong understanding of search relevance metrics (e.g., recall@k, latency) and performance optimization techniques.
• Experience partnering with cross-functional teams such as Sales, PreSales, Services, and Support.

Nice to Have

• Experience with large-scale deployment of enterprise search and vector search systems.
• Familiarity with cloud-native search architectures and tools.
• Knowledge of AI/ML models related to semantic and vector search.

Qualifications

• Formal educational background is not explicitly specified.

Benefits & Perks

• Not specified in the description.