Experteer Overview
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In this role you design, build, and deploy production-grade agentic AI systems for enterprise environments. You will collaborate with client engineering teams, lead technical design sessions, and create reusable patterns to scale across engagements. You operate at the intersection of AI engineering and client delivery, ensuring robust and observable systems that perform in production. This is a chance to shape enterprise AI by building multi-agent architectures and driving end-to-end implementation with cross-functional teams.
Compensaciones / Beneficios
• Design and build end-to-end production-grade agentic systems (multi-agent orchestration, RAG pipelines, policy routing, tool invocation, memory management, observability)
• Own RAG pipelines (embeddings, chunking, vector search, context engineering, quality targeting)
• Integrate across LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) with routing, token, cost, and latency management
• Implement LLMOps in production (eval harnesses, prompt versioning, observability tooling, cost and safety monitoring)
• Collaborate directly with client teams to prototype and deploy agentic solutions (workshops, proofs of concept, code-with sessions, architecture walkthroughs)
• Build reusable patterns,



accelerators, and playbooks to scale beyond individual engagements
• Define metrics for agent accuracy, latency, safety, and cost-effectiveness; present findings to client stakeholders
Responsabilidades
• Software engineering experience in production environments
• Hands-on experience designing and deploying agentic AI solutions in production
• Experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent at production depth
• Direct experience calling LLM APIs in production (OpenAI, Anthropic, Vertex AI)
• RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
• xqbhyrx LLMOps fundamentals (eval harness design, prompt versioning, production observability)
• Cloud-native engineering maturity (Kubernetes, Docker, microservices, serverless, CI/CD, IaC)
• Strong Python; Java or equivalent backend language; production debugging and observability experience
• Preference for shipping multiple production agentic systems within years
Requisitos principales
• vendor fellowship access inside leading AI teams
• pathway to Forward Deployed Engineer programme
• learning and certifications opportunities
• inclusive and diverse workplace
• well-being support
• global opportunities across major cities

📌 AI Native Engineer (Agentic / Applied) (Madrid)
🏢 Accenture
📍 Madrid