Opportunity at
ElasticAgentic AI Engineer
This full-time, on-site Agentic AI Engineer role sits within Elastic's IT team and focuses on agentic workflows and agent-to-agent systems. The posting describes the goal as…
Career Tools
About This Role
This full-time, on-site Agentic AI Engineer role sits within Elastic's IT team and focuses on agentic workflows and agent-to-agent systems. The posting describes the goal as building multi-agent systems in which agents communicate, delegate tasks, share information, use enterprise tools, and complete business processes from start to finish, rather than only answering questions. The engineer would design and deploy agent-to-agent architectures, connect large language models, both proprietary and open-source, to…
Job Description
This full-time, on-site Agentic AI Engineer role sits within Elastic's IT team and focuses on agentic workflows and agent-to-agent systems. The posting describes the goal as building multi-agent systems in which agents communicate, delegate tasks, share information, use enterprise tools, and complete business processes from start to finish, rather than only answering questions.
The engineer would design and deploy agent-to-agent architectures, connect large language models, both proprietary and open-source, to enterprise APIs and third-party SaaS applications using tool calling, and apply retrieval-augmented generation grounded in search and vector storage. The work also includes provisioning cloud environments on AWS, Azure, or GCP with Terraform, building CI/CD pipelines, applying security controls to inter-agent communication, and monitoring agent interactions, token spend, model drift, and agent loops.
The posting asks for advanced Python or TypeScript skills, experience with multi-agent frameworks such as LangGraph, LangChain, or AutoGen, awareness of emerging agent interoperability specs, including MCP, and hands-on work with Docker and Kubernetes. Writing technical documentation for integration protocols and cloud deployments is part of the role. The posting also describes a service-oriented mindset for supporting internal teams.
Responsibilities
- Design, build, and deploy agent-to-agent communication architectures and multi-step workflows
- Integrate LLMs with enterprise APIs and third-party SaaS applications using tool calling
- Apply retrieval-augmented generation that connects agents to enterprise context through search and vector storage
- Provision and manage cloud environments on AWS, Azure, or GCP with Terraform
- Build CI/CD pipelines for testing, deployment, and versioning, and regularly evaluate LLMs and agentic workflows
- Apply security and network controls to inter-agent communication and sensitive enterprise data
- Create observability and evaluation systems for multi-agent behavior, token costs, and model drift
- Keep technical documentation for LLM integration protocols and cloud deployments
Requirements
- Substantial background integrating, adapting, and tuning foundation models, covering prompt design, tool calls, and structured outputs
- Proven experience building multi-agent systems, inter-agent messaging, state management, and task delegation
- Hands-on use of multi-agent frameworks like LangGraph, LangChain, or AutoGen, plus LangSmith for tracing
- Strong command of Python or TypeScript
- Working grasp of RAG designs, vector stores, hybrid retrieval, and context handling
- Hands-on experience with Terraform, Docker, and Kubernetes
- Strong knowledge of secure cloud design, zero-trust networking, private endpoints, and identity management with OAuth, SAML, and IAM
- Experience setting up logging, distributed tracing, and metrics for non-deterministic multi-agent workflows
- Understanding of emerging interoperability specifications, including MCP
Qualifications
- Knowledge of the wider enterprise AI platform landscape, such as Workday A2A, Salesforce Agentforce, and ServiceNow AI agents
Core Skills
Benefits
- Eligibility for Elastic's stock program
- 401(k) employer match, dollar for dollar, capped at 6% of eligible pay
- Pay set by the role itself rather than by past earnings
- Health coverage for employees and families in many locations
- Flexible scheduling options for many roles
- Generous annual vacation allowance
- Match of up to $2,000 for financial donations and service, plus up to 40 volunteer hours per year
- Parental leave of at least 16 weeks
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is this role located?
The listing gives the location as United States and marks the role as on-site. The posting does not name a specific city.
Is this role remote?
No. The posting lists the role as on-site.
What is the pay for this role?
The posting gives a typical starting base salary of $94,300 to $149,200 USD. In select locations listed in the posting, the typical starting range is $113,300 to $179,200 USD. Compensation is stated as base salary.
What experience does the employer look for?
The posting asks for hands-on experience building multi-agent systems with LLMs, advanced Python or TypeScript skills, and infrastructure-as-code work with Terraform, Docker, and Kubernetes.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.