Agentic AI Engineer

You will design and deploy agent-to-agent systems that automate complex workflows across Elastic's enterprise platforms. This role involves integrating large language models with internal tools, ensuring secure and scalable communication between agents, and building systems that enhance productivity through intelligent automation.

Summarised by InternDoor from Elastic’s posting, in our own words. The posting itself is the source of truth, so check it before you apply.

• Build agent-to-agent systems that automate complex business processes
• Integrate LLMs with enterprise tools and APIs using Terraform and cloud platforms
• Design secure, scalable architectures for multi-agent collaboration and data sharing

Required

• Experience Experience with AI-assisted development, code review, test generation, working through unfamiliar code, drafting specifications, summarizing incidents, and accelerating research
• LLM & GenAI Expertise Deep experience integrating, fine-tuning, and optimizing foundation models (e.g., OpenAI, Anthropic, open-source models), including expertise in prompt engineering, tool calling, and structured outputs
• Agent-to-Agent (A2A) Protocols Proven success building multi-agent systems, inter-agent messaging pipelines, state management frameworks, and dynamic task delegation protocols
• Agentic Frameworks Hands-on experience with multi-agent orchestration frameworks such as LangGraph, LangChain, AutoGen, etc. along with tracing platforms like LangSmith
• Security & Network Fundamentals Strong knowledge of secure cloud architectures, zero-trust network design, private endpoints, and identity management (OAuth, SAML, IAM) for secure A2A interactions
• LLM Observability & Evaluation Experience setting up observability frameworks (logging, distributed tracing, and metrics) to track non-deterministic multi-agent workflows, token costs, and LLM output quality

Nice to have

• Market Trends & Interoperability Strong understanding of emerging AI interoperability standards, such as the Model Context Protocol (MCP) and open multi-agent communication specifications
• Enterprise AI Platform Knowledge Knowledge of the broader agentic and workflow landscape (e.g., Workday A2A, Salesforce Agentforce, ServiceNow AI Agents)

Working conditions

• Enterprise Grounding & Vector Search Practical knowledge of RAG patterns, vector databases, hybrid search architectures, and context management strategies

Read from Elastic’s posting. Its own wording is the one that counts.

1. Check what they ask for. Read the requirements above.
2. Tailor your resume to the role. Put the work that matches these skills first. InternDoor’s resume tailor does it against this posting.
3. Apply on Elastic’s careers site. The application is on jobs.elastic.co, not on InternDoor. We never ask for a fee.

Apply soon: postings like this can close without notice, and this one states no deadline.

Posted

11 Nov 2026, on LinkedIn

First seen by InternDoor

11 Nov 2026, within 2 hours of being posted

Last checked

11 Nov 2026: the application page loaded. That shows the page is up, not that Elastic is still filling the role.

Application deadline

Elastic has not stated one

Leaves InternDoor

By 11 Dec 2026, our 30-day limit for LinkedIn listings, or sooner if the application closes

Application page

jobs.elastic.co

What we checked, and what the dates mean

The date this listing leaves InternDoor is our own rule, not a deadline set by Elastic. Postings can close earlier, at any time.

What “last checked” means: our checker opened the application page and it answered. That shows the page is up; it does not prove the role is still being filled, and Elastic has not confirmed anything to us.

InternDoor has recorded 2 engineering postings from Elastic in India since Oct 2026.

When this listing comes down, this address says the role has closed and lists open roles like it, instead of a dead end.

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