Sr. AI Engineer, Application Engineering
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to
What this role actually needs.
Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to Requirements: - Data Configuration and Integration: Own the full data lifecycle, from data model design to building efficient processing pipelines and establishing integration strategies. Ensure data is optimized and secure for AI applications, including in complex enterprise environments. - Technical Problem Solving: Identify, analyze, and resolve technical challenges across all phases of solution delivery, from data integration to model deployment and agent orchestration. Serve as a reliable resource for unblocking progress. - Agentic Innovation: Develop expertise in the Elastic platform, pushing its capabilities forward. Lead the development of custom intelligent agents, automate business processes, and shape user experiences. Insights from the field will directly influence product enhancements and platform direction. - Client Partnership: Embed with client teams to understand their operational challenges and goals. Translate requirements into clear technical designs, build strong relationships, and serve as a trusted technical advisor. - Debugging and Root Cause Analysis: Perform thorough analysis, debugging, and root cause identification for complex system interactions, data flows, and AI model behaviors to optimize performance and prevent recurring issues. - Prototyping and Iteration: Rapidly develop proofs-of-concept and minimum viable products, often coding alongside client teams to demonstrate capabilities and gather feedback for iterative refinement. Company context: Elastic is the public observability and search company behind Elasticsearch, Kibana, and the Elastic Stack.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Data Configuration and Integration: Own the full data lifecycle, from data model design to building efficient processing pipelines and establishing integration strategies. Ensure data is optimized and secure for AI applications, including in complex enterprise environments.
- Technical Problem Solving: Identify, analyze, and resolve technical challenges across all phases of solution delivery, from data integration to model deployment and agent orchestration. Serve as a reliable resource for unblocking progress.
- Agentic Innovation: Develop expertise in the Elastic platform, pushing its capabilities forward. Lead the development of custom intelligent agents, automate business processes, and shape user experiences. Insights from the field will directly influence product enhancements and platform direction.
- Client Partnership: Embed with client teams to understand their operational challenges and goals. Translate requirements into clear technical designs, build strong relationships, and serve as a trusted technical advisor.
- Debugging and Root Cause Analysis: Perform thorough analysis, debugging, and root cause identification for complex system interactions, data flows, and AI model behaviors to optimize performance and prevent recurring issues.
- Prototyping and Iteration: Rapidly develop proofs-of-concept and minimum viable products, often coding alongside client teams to demonstrate capabilities and gather feedback for iterative refinement.
Why this listing is more than a copied job post.
Sr. AI Engineer, Application Engineering is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.
United States tech market
United States roles on UpJobz are filtered for high-tech relevance, source freshness, and actionable employer detail before they are allowed into SEO surfaces.
Compensation read
The employer source does not expose a reliable salary range, so candidates should ask for compensation early instead of waiting until late-stage interviews.
Work authorization read
Current extracted signal: Open to TN, H-1B, and OPT candidates already in the United States. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
Location read
Because this is remote, country scope and time-zone expectations matter as much as the title. Confirm the employer's allowed work locations on jobs.elastic.co.
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- Compensation is hidden, so get range clarity in the first recruiter conversation.
- Use open to tn, h-1b, and opt candidates already in the united states as part of your positioning so the recruiter does not have to infer it.
- Lead with distributed collaboration, async delivery, and timezone discipline.
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Source: jobs.elastic.co · Source ID: 7722032 · Confidence: 89/100 · Last checked: May 7, 2026
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