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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Ai Software Engineer | Spain - Barcelona - **Company:** Accenture - **Location:** Barcelona, Spain - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Cloud Engineering, Continuous Integration, Software Debugging, Memory Management, Python (Programming Language), Open Source Technology, Search Technologies, Software Engineering, Management of Software Versions, Datadog, Large Language Models, Multi-Agent Systems, Backend, Build Management, Kubernetes, Low Latency, Production Code, Machine Learning Operations, Terraform, Serverless Computing, Docker, Microservices - **Published:** September 29, 2026 - **Apply:** https://www.buscojobs.com.es/ai-software-engineer-spain-barcelona-en-barcelona-ID-373337122 ## About the Role metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms Requisitos principales Strong software engineering experience in production environments Hands-on experience designing and deploying agentic AI solutions in a production environment Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm) Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure LangGraph, CrewAI, AutoGen or equivalent agentic orchestration frameworks LLM APIs (OpenAI, Anthropic, Vertex AI) in production RAG pipelines: embeddings, chunking, vector databases, context engineering LLMOps: eval harnesses, prompt versioning, production observability Kubernetes, Docker, microservices, serverless, CI/CD, IaC (Terraform or Helm) Python; Java or equivalent backend language ## Description Overview In this role you design, build, and deploy production-grade agentic AI systems across the enterprise stack, collaborating directly with client engineering teams.You own end-to-end orchestration, RAG pipelines, and multi-provider integration to scale across engagements.You will implement LLMOps, observability, and cost/safety monitoring while developing reusable patterns and accelerators that accelerate future work.This is a hands-on, client-facing opportunity to shape enterprise AI solutions at scale.Responsabilidades Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering Integrate and abstract across multiple LLM providers - OpenAI, Anthropic, Vertex AI, and open-source models - with fallback routing, token, cost, and latency management Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring Embed directly with client engineering teams to design, prototype, and deploy agentic solutions - workshops, proofs of concept, code-with sessions, and architecture walkthroughs Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness; present findings and recommendations to client stakeholders in business terms Requisitos principales Strong software engineering experience in production environments Hands-on experience designing and deploying agentic AI solutions in a production environment Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering LLMOps fundamentals: eval harness design, prompt versioning, and production observability Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm) Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure LangGraph, CrewAI, AutoGen or equivalent agentic orchestration frameworks LLM APIs (OpenAI, Anthropic, Vertex AI) in production RAG pipelines: embeddings, chunking, vector databases, context engineering LLMOps: eval harnesses, prompt versioning, production observability Kubernetes, Docker, microservices, serverless, CI/CD, IaC (Terraform or Helm) Python; 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