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About the Role
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.
Key Technical Responsibilities
- check_circle 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.
- check_circle Integrate advanced LLMs, both proprietary and open-source, with enterprise APIs using tool-calling methods and connecting with third-party SaaS applications.
- check_circle Use advanced Retrieval-Augmented Generation (RAG) techniques and enterprise context strategies with search and vector storage engines for high accuracy and reliability.
- check_circle Provision and manage robust cloud environments (AWS, Azure, GCP) using Terraform to support the high-concurrency demands of LLM inferences and multi-agent coordination.
- check_circle Support modern DevOps practices by creating CI/CD pipelines for automated testing, deployment, versioning, and regular evaluation of LLMs and agentic workflows.
- check_circle 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.
- check_circle Create systems to monitor multi-agent interactions to check model accuracy, manage token spending, and identify model drift or agent loops.
- check_circle Keep detailed technical documents for LLM integration protocols, A2A interaction flows, and cloud infrastructure deployments.
Prerequisites & Qualifications
- check 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.
- check Proven success building multi-agent systems, inter-agent messaging pipelines, state management frameworks, and dynamic task delegation protocols.
- check Hands-on experience with multi-agent orchestration frameworks such as LangGraph, LangChain, AutoGen, and tracing platforms like LangSmith.
- check Strong understanding of emerging AI interoperability standards, such as the Model Context Protocol (MCP) and open multi-agent communication specifications.
- check Advanced proficiency in Python or TypeScript for backend orchestration, agent memory systems, and API service development.
- check Practical knowledge of RAG patterns, vector databases, hybrid search architectures, and context management strategies.
- check Hands-on experience with DevOps practices and infrastructure automation using Terraform, Docker, and Kubernetes for scalable AI deployments.
- check Strong knowledge of secure cloud architectures, zero-trust network design, private endpoints, and identity management (OAuth, SAML, IAM) for secure A2A interactions.
- check Experience setting up observability frameworks to track non-deterministic multi-agent workflows, token costs, and LLM output quality.
- check Knowledge of the broader agentic and workflow landscape (e.g., Workday A2A, Salesforce Agentforce, ServiceNow AI Agents).
About Elastic Greenhouse Board
Verified hiring organization details.
Elastic Greenhouse Board is a technology and software company based in the United States, operating as an active hiring employer.