Elastic
Enterprise · Search
- HQ
- Mountain View, US
- Founded
- 2012
- Website
- elastic.co
Enterprise · Search
Elastic currently has 12 active AI-related roles in our index. The most common open titles are: Principal Product Manager, AI agents - Search (3), Elastic AI Engineer (2), Principal Analytics Engineer (2), Principal Software Engineer - Vector Search - Elasticsearch (2), Senior Software Engineer - Search Relevance - Elasticsearch (2). Most positions are in Engineering and Product.
Elastic's active AI hiring is concentrated in: agents (100%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Elastic is hiring AI talent in: United States (10 roles), United Kingdom (2 roles).
Job postings at Elastic most frequently reference: agent orchestration, vector db, rag, llm observability, search ranking.
In the past 30 days, Elastic has posted 5 new AI-related roles. That is a -44% change versus the prior 30 days (9 → 5).
Currently tracking 9 active AI roles, down 37% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $192k–$403k (avg $278k).
Elastic has 12 active AI-related job listings. All of these roles are focused on agents, representing 100% of their current AI hiring. The majority of these positions are in Engineering, with some in Product. Hiring is concentrated in the United States and the United Kingdom. Frequent tech tags include agent orchestration, RAG, and vector databases. Over the last 30 days, Elastic has posted 11 new AI roles, a 450% increase from the previous 30-day period.
| Title | Stage | AI score |
|---|---|---|
| Elastic Consulting Architect This role is for an Elastic Consulting Architect who will work with customers to provide technical solutions using the Elastic Stack for enterprise search, observability, and security use cases. The architect will act as a trusted technical advisor, driving customer adoption and delivering end-to-end projects and proof-of-concepts. The role involves data modeling, query optimization, cluster tuning, and collaborating with internal teams to provide feedback for product improvements. | — | 5 |