Role Description & Responsibilities
Artificial Intelligence
About Elastic
Elastic is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Artificial Intelligence role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
These ranges represent the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the ranges may be modified in the future.
Key Responsibilities & Day-to-Day Scope
- ✓ 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.
- ✓ 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.
Official Qualifications & Technical Requirements
- • 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).
Preferred Skills & Technology Stack
- + Experience fine-tuning small language models (LoRA, QLoRA) on domain-specific datasets.
- + Exposure to cloud platforms (AWS, GCP, Azure) and container deployment (Docker).
- + Active portfolio of deployed AI applications or hackathon projects.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
Generative AI & LLMsPrompt EngineeringLangChain & Vector DBsFastAPI Data APIs
★ Life & Culture at Elastic
Verified EmployerElastic champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.