Key skills
Elastic Stack expertiseElasticsearch Relevance EngineAI Model IntegrationCloud environmentsProgramming (Python/TypeScript)Agentic FrameworksContainerization (Docker/Kubernetes)Model ExpertiseEthical AI commitmentTechnical Leadershipcommunication skillsTypeScriptPythonAIGenAILLMLangChainLangGraphAWSAzureGCPTerraformKubernetesDockerElasticsearchPerformance OptimizationServiceNowSaaSSalesforceCommunication
About this role
Elastic, the Search AI Company, is seeking an innovative Elastic AI Engineer to join their team. This role involves building autonomous agents that complete complex business tasks, leveraging the Elastic Stack to enhance enterprise productivity and efficiency.
Responsibilities:
- Invent and implement sophisticated agentic workflows that use reasoning and tools to complete end-to-end business processes
- 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
- Develop and fine-tune LLMs and integrate them with internal APIs and third-party SaaS tools to enable autonomous action
- Firm understanding of cloud-based environments (AWS, Azure, GCP) in order to support the high-concurrency demands of enterprise agents
- Oversee the training, deployment, and performance optimization of agents, ensuring they remain secure, reliable, and compliant
- Act as a domain expert on the Elastic Stack, making technical recommendations that push the boundaries of AI-driven productivity
- Maintain comprehensive documentation of AI workflows, cloud infrastructure, and deployment processes
- 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
- 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