The ideal candidate is an Elastic product expert (including but not limited to Agent Builder and Workflows), using the full power of the Elastic Stack to provide the 'brain' and 'memory' for our agentic ecosystem.

As the company behind the popular open-source projects , Elasticsearch, Kibana, Logstash, and Beats , we help people around the world do great things with their data.

The Elastic family unites employees across 40+ countries into one coherent team, while the broader community spans across over 100 countries.

Responsibilities

• 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.

Requirements

• 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.

• Agentic Frameworks: Familiarity with LangGraph, LangChain, and LangSmith for building and debugging multi-agent systems.

• Expertise in Enterprise Agentic & Workflow Platforms: Deep familiarity with leading agentic AI and workflow automation platforms (such as Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow AI Agents).

• Market Trend Integration: Proven ability to apply emerging market trends,such as Multi-Agent Orchestration and Model Context Protocol (MCP),to build high-impact, cost-optimized solutions that scale across the enterprise.

• Programming: Experience with Python or TypeScript for backend logic and agent orchestration.

• Cloud & Orchestration: Familiarity with Kubernetes (Operators/Controllers), Docker, and Terraform for automated deployment.

• Model Expertise: Hands-on experience with LLM providers.

Bonus Points

• Bachelor’s or Master’s degree in Computer Science or a related engineering field.

• Strong communication skills with the ability to translate business requirements into technical agent architectures.

• A commitment to Ethical AI and responsible development practices.

• Experience with containerization and orchestration (e.g., Docker, Kubernetes).

• Knowledge of DevOps practices for model deployment and automation.

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As the company behind the popular open-source projects , Elasticsearch, Kibana, Logstash, and Beats , we help people around the world do great things with their data. From stock quotes to Twitter streams, Apache logs to WordPress blogs, our products are extending what's possible with data, delivering on the promise that good things come from connecting the dots.

Responsibilities

• 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.

Requirements

• 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.
• Agentic Frameworks: Familiarity with LangGraph, LangChain, and LangSmith for building and debugging multi-agent systems.
• Expertise in Enterprise Agentic & Workflow Platforms: Deep familiarity with leading agentic AI and workflow automation platforms (such as Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow AI Agents.)
• Market Trend Integration: Proven ability to apply emerging market trends,such as Multi-Agent Orchestration and Model Context Protocol (MCP),to build high-impact, cost-optimized solutions that scale across the enterprise.
• Programming: Experience with Python or TypeScript for backend logic and agent orchestration.
• Cloud & Orchestration: Familiarity with Kubernetes (Operators/Controllers), Docker, and Terraform for automated deployment.
• Model Expertise: Hands-on experience with LLM providers.

Bonus Points

• Bachelor’s or Master’s degree in Computer Science or a related engineering field.
• Strong communication skills with the ability to translate business requirements into technical agent architectures.
• A commitment to Ethical AI and responsible development practices.
• Experience with containerization and orchestration (e.g., Docker, Kubernetes).
• Knowledge of DevOps practices for model deployment and automation.

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