Ai Software Engineer | Spain

Experteer 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.Compensaciones / Beneficios - 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 termsResponsabilidades - 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 exposureRequisitos principales -

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.Compensaciones / Beneficios - 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 termsResponsabilidades - 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 exposureRequisitos principales -