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