Elastic
Enterprise · Search
- HQ
- Mountain View, US
- Founded
- 2012
- Website
- elastic.co
Currently tracking 9 active AI roles, up 58% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $192k–$403k (avg $278k).
Enterprise · Search
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.
Elastic currently has 25 active AI-related roles in our index. The most common open titles are: Principal Product Manager, AI agents - Search (7), Principal Data Scientist - Agent Builder (6), Elastic AI Engineer (2), Principal Search Consulting Architect (2), Principal Software Engineer - Vector Search - Elasticsearch (2). Most positions are in Engineering and Product.
Elastic's active AI hiring is concentrated in: agents (92%), application (4%), data (4%). 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 (12 roles), Netherlands (6 roles), United Kingdom (2 roles), Canada (2 roles).
Job postings at Elastic most frequently reference: rag, llm observability, agent orchestration, vector db, evals.
In the past 30 days, Elastic has posted 16 new AI-related roles. That is a +78% change versus the prior 30 days (9 → 16).
| Title | Stage | AI score |
|---|---|---|
| Senior AI Data Engineer Senior AI Data Engineer responsible for building and maintaining the golden customer dataset, making it AI-ready for downstream AI workflows (account research, lead scoring, churn signals, CSM briefings). This involves designing canonical datasets, implementing enrichment pipelines, deduplication, entity resolution, validation systems, chunking, embedding strategy, metadata design, and source integration. The role also owns quality, lineage, monitoring, drift detection, and documentation for AI consumption. Requires experience with GTM data, preparing data for RAG, embeddings, and AI agents, and using LLMs for data tasks. Experience with Python, SQL, cloud infrastructure, orchestration, and Elastic Stack is necessary. | DataAgent | 7 |