What's the tech stack?

Joblaze extracted these technologies from the posting: AWS, Anthropic, AutoGen, Azure, Docker, GCP.

What seniority level is this role?

Elastic targets mid-level candidates for this position.

Is this full-time or contract?

Full-time for this Agentic AI Engineer role at Elastic.

Agentic AI Engineer at Elastic

Joblaze summary

The Agentic AI Engineer at Elastic focuses on developing multi-agent ecosystems that facilitate collaboration and task delegation among autonomous agents. This role requires expertise in large language models, agent orchestration, and integration with enterprise systems, utilizing tools like Terraform and Python. It is suited for professionals with a strong background in AI, particularly those experienced in building complex agent-to-agent communication frameworks. The position is part of a team pushing the boundaries of AI capabilities within a well-established company.

Joblaze insights

Listed today — first seen on Joblaze October 6, 2026. Last confirmed on Elastic's careers page October 6, 2026.
This exact title is also open at 1 other location at Elastic: United States.

Quick facts

What's the tech stack?: Joblaze extracted these technologies from the posting: AWS, Anthropic, AutoGen, Azure, Docker, GCP.
What seniority level is this role?: Elastic targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Agentic AI Engineer role at Elastic.

From the original posting

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 enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role

The Elastic IT team is moving beyond simple chat to the next frontier: Agentic Workflows and Agent-to-Agent (A2A) Systems. We are looking for an innovative Agentic AI Engineer to join our team. You will build multi-agent ecosystems that are self-directed. These ecosystems will do more than just answer questions. They will collaborate, delegate tasks, and execute complex business processes across the organization.

In this role, you will focus on large language models (LLMs), agent orchestration, and A2A integration frameworks to power our intelligent workforce. You will create workflows for multiple agents instead of just focusing on single-agent search or managing the stack. These workflows will enable the agents to communicate with each other, share information, access enterprise tools, and automate tasks from start to finish. Driven by a service-oriented mindset, you will empower internal teams with cutting-edge LLM capabilities while integrating seamlessly with enterprise systems.

Are you ready to build the next generation of LLM-powered multi-agent systems that supercharge enterprise productivity? Join us to enable agent-to-agent workflows that turn collective knowledge into instant, coordinated action across Elastic.

What You Will Be Doing

A2A Architecture & Multi-Agent Orchestration: Design, build, and deploy agent-to-agent (A2A) communication architectures and workflows that enable autonomous agents to collaborate, delegate tasks, and negotiate multi-step business processes.
LLM Integration & Tool Calling: Integrate advanced LLMs, both proprietary and open-source, with enterprise APIs. Use tool-calling methods. Connect with third-party SaaS applications for smooth operations.
Enterprise Grounding & RAG: Use advanced Retrieval-Augmented Generation (RAG) techniques. Implement strategies that connect enterprise context by using search and vector storage engines. This will ensure that all inter-agent operations are highly accurate and reliable.
Scalable Infrastructure & IaC: Provision and manage robust cloud environments (AWS, Azure, GCP) using Terraform to support the high-concurrency demands of LLM inferences and multi-agent coordination.
DevOps & Lifecycle Management: Support modern DevOps practices. This includes creating CI/CD pipelines for automated testing, deployment, and versioning. You will also evaluate LLMs and agentic workflows regularly.
A2A Security & Governance: Apply strict security and network fundamentals (VPC configurations, secure API gateways, encryption, and IAM controls) to secure inter-agent communication and protect sensitive enterprise data.
LLM Observability & Evaluation: Create systems to monitor how multiple agents interact. These systems will help us check model accuracy. They will also help manage token spending and identify model drift or agent loops.
Documentation: Keep detailed technical documents for LLM integration protocols. This includes A2A interaction flows and cloud infrastructure deployments.

What You Bring

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.
Market Trends & Interoperability: Strong understanding of emerging AI interoperability standards, such as the Model Context Protocol (MCP) and open multi-agent communication specifications.
Programming: Advanced proficiency in Python or TypeScript for backend orchestration, agent memory systems, and API service development.
Enterprise Grounding & Vector Search: Practical knowledge of RAG patterns, vector databases, hybrid search architectures, and context management strategies.
DevOps & Infrastructure as Code (IaC): Hands-on experience with DevOps practices and infrastructure automation using Terraform, Docker, and Kubernetes for scalable AI deployments.
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.
Enterprise AI Platform Knowledge: Knowledge of the broader agentic and workflow landscape (e.g., Workday A2A, Salesforce Agentforce, ServiceNow AI Agents).

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