Agentic Strategy Design: Invent and implement sophisticated agentic workflows that use reasoning and tools to complete end-to-end business processes.

Enterprise Grounding: Apply Retrieval Augmented Generation (RAG) and the Elasticsearch Relevance Engine (ESRE) to ensure agents are deeply grounded in enterprise knowledge for high-accuracy task completion.

AI Model Tool Integration: Develop and fine-tune LLMs and integrate them with internal APIs and third-party SaaS tools to enable autonomous action.

Scalable Infrastructure: Firm understanding of cloud-based environments (AWS, Azure, GCP) in order to support the high-concurrency demands of enterprise agents.

Lifecycle Management: Oversee the training, deployment, and performance optimization of agents, ensuring they remain secure, reliable, and compliant.

Technical Leadership: Act as a domain expert on the Elastic Stack, making technical recommendations that push the boundaries of AI-driven productivity.

Documentation: Maintain comprehensive documentation of AI workflows, cloud infrastructure, and deployment processes.

Security: Implement standards for security and data privacy to protect sensitive information and ensure compliance with relevant regulations.

3-5 years of work experience in a relevant field.

Minimum 1 year experience building with the Elastic Stack.

Knowledge of Elasticsearch Relevance Engine (ESRE), Jina AI, and advanced RAG patterns is critical.

Proven success in delivering independent GenAI projects, specifically those involving autonomous task completion or complex workflow automation.

Elastic AI Engineer

Responsibilities

Qualification

Required

Benefits

Company

Elastic

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