Elastic - Agentic AI Engineer
Requirements
• LLM & GenAI Expertise: Deep experience integrating, fine-tuning, and optimizing foundation models (e.g., OpenAI, Anthropic, open-source models), including expertise in prompt engineering, tool calling, and structured outputs. • Agent-to-Agent (A2A) Protocols: Proven success building multi-agent systems, inter-agent messaging pipelines, state management frameworks, and dynamic task delegation protocols. • Agentic Frameworks: Hands-on experience with multi-agent orchestration frameworks such as LangGraph, LangChain, AutoGen, etc. along with tracing platforms like LangSmith. • Market Trends & Interoperability: Strong understanding of emerging AI interoperability standards, such as the Model Context Protocol (MCP) and open multi-agent communication specifications. • Programming: Advanced proficiency in Python or TypeScript for backend orchestration, agent memory systems, and API service development. • Enterprise Grounding & Vector Search: Practical knowledge of RAG patterns, vector databases, hybrid search architectures, and context management strategies. • DevOps & Infrastructure as Code (IaC): Hands-on experience with DevOps practices and infrastructure automation using Terraform, Docker, and Kubernetes for scalable AI deployments. • Security & Network Fundamentals: Strong knowledge of secure cloud architectures, zero-trust network design, private endpoints, and identity management (OAuth, SAML, IAM) for secure A2A interactions. • LLM Observability & Evaluation: Experience setting up observability frameworks (logging, distributed tracing, and metrics) to track non-deterministic multi-agent workflows, token costs, and LLM output quality. • Enterprise AI Platform Knowledge: Knowledge of the broader agentic and workflow landscape (e.g., Workday A2A, Salesforce Agentforce, ServiceNow AI Agents). • Additional Information - We Take Care of Our People: • As a distributed company, diversity drives our identity. Whether you’re looking to launch a new career or grow an existing one, Elastic is the type of company where you can balance great work with great life. Your age is only a number. It doesn’t matter if you’re just out of college or your children are; we need you for what you can do. • We strive to have parity of benefits across regions, and while regulations differ from place to place, we believe taking care of our people is the right thing to do. • Competitive pay based on the work you do here and not your previous salary • Health coverage for you and your family in many locations • Ability to craft your calendar with flexible locations and schedules for many roles • Generous number of vacation days each year • Increase your impact - We match up to $2000 (or local currency equivalent) for financial donations and service • Up to 40 hours each year to use toward volunteer projects you love • Embracing parenthood with a minimum of 16 weeks of parental leave
Responsibilities
• A2A Architecture & Multi-Agent Orchestration: Design, build, and deploy agent-to-agent (A2A) communication architectures and workflows that enable autonomous agents to collaborate, delegate tasks, and negotiate multi-step business processes. • LLM Integration & Tool Calling: Integrate advanced LLMs, both proprietary and open-source, with enterprise APIs. Use tool-calling methods. Connect with third-party SaaS applications for smooth operations. • Enterprise Grounding & RAG: Use advanced Retrieval-Augmented Generation (RAG) techniques. Implement strategies that connect enterprise context by using search and vector storage engines. This will ensure that all inter-agent operations are highly accurate and reliable. • Scalable Infrastructure & IaC: Provision and manage robust cloud environments (AWS, Azure, GCP) using Terraform to support the high-concurrency demands of LLM inferences and multi-agent coordination. • DevOps & Lifecycle Management: Support modern DevOps practices. This includes creating CI/CD pipelines for automated testing, deployment, and versioning. You will also evaluate LLMs and agentic workflows regularly. • A2A Security & Governance: Apply strict security and network fundamentals (VPC configurations, secure API gateways, encryption, and IAM controls) to secure inter-agent communication and protect sensitive enterprise data. • LLM Observability & Evaluation: Create systems to monitor how multiple agents interact. These systems will help us check model accuracy. They will also help manage token spending and identify model drift or agent loops. • Documentation: Keep detailed technical documents for LLM integration protocols. This includes A2A interaction flows and cloud infrastructure deployments.
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