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; Java or equivalent backend language

📌 AI Software Engineer | Spain (Barcelona)
🏢 Accenture
📍 Barcelona