Agentic AI Engineer

Elastic's internal IT team is moving beyond simple chat to agentic workflows, autonomous, enterprise-grounded agents that complete complex business tasks rather than just answering questions. As an Agentic AI Engineer you will build directly on the Elastic Stack, using ESRE, Agent Builder, and Workflows as the brain and memory of the agentic ecosystem, and act as a trusted enabler helping internal business partners solve their own data problems with the platform.

Responsibilities

• Design and implement agentic workflows that use reasoning and tools to execute end-to-end business processes.
• Ground agents in enterprise knowledge with RAG and the Elasticsearch Relevance Engine (ESRE) for high-accuracy task completion.
• Develop and fine-tune LLMs and integrate them with internal APIs and third-party SaaS tools to enable autonomous action.
• Provision and manage cloud environments (AWS, Azure, GCP) with Terraform to support high-concurrency enterprise agents.
• Establish CI/CD pipelines for automated deployment, testing, and performance optimization of agents.
• Apply strict security and network fundamentals, including VPC configurations, secure API gateways, encryption, and IAM controls.
• Implement observability and tracing to monitor agent behavior, track execution latency, manage token spend, and detect model drift.
• Act as a subject matter expert on the Elastic Stack and maintain documentation of AI workflows and infrastructure.

Qualifications

• Proven success delivering independent GenAI projects involving autonomous task completion or complex workflow automation.
• Familiarity with LangGraph, LangChain, and LangSmith for building and debugging multi-agent systems.
• Hands-on DevOps and infrastructure-as-code experience with Terraform, Docker, and Kubernetes.
• Strong knowledge of secure cloud architectures, private networking, and identity management (OAuth, SAML, IAM).
• Experience with observability frameworks (logging, distributed tracing, metrics) for non-deterministic multi-agent workflows.
• Ability to apply emerging patterns such as multi-agent orchestration and Model Context Protocol (MCP) to cost-optimized solutions.
• Programming experience in Python or TypeScript for backend logic and agent orchestration, plus hands-on experience with LLM providers.
• Bachelor's or Master's degree in Computer Science or a related engineering field, and a commitment to ethical AI practices.