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## 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 automation.
## Nice to Have
- Experience with enterprise AI platforms and large-scale deployment.
- Strong understanding of AI ethics and responsible AI practices.
- Experience with DevOps practices and CI/CD pipelines.
- Familiarity with data governance and compliance frameworks.
## Benefits
- The Elastic family unites employees across 40+ countries into one coherent team, while the broader community spans across over 100 countries.
- 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.
- Elastic enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people.
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