Overview
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As an AI Engineer (Agentic/Applied), you design, build, and deploy production-grade agentic AI systems for enterprise-scale environments. You collaborate with client engineering teams, lead technical design sessions, and create reusable patterns to scale across engagements. This role sits at the core of Accenture’s AI engineering practice, offering breadth across industries and direct access to leading AI teams. You will shape production systems, governance, and observability, delivering measurable business impact.
Compensaciones / Incentivos
vendor fellowship access inside leading AI teams (Anthropic, OpenAI, Microsoft, Google)
broad industry exposure and enterprise-scale impact
career development and leadership opportunities
full-time, global team collaboration
Responsabilidades
Architect and govern production-grade agentic systems at enterprise scale (multi-agent orchestration, RAG pipelines, memory management, lifecycle observability)
Define RAG pipeline standards, chunking/embedding strategies, quality benchmarks, and transferable metrics
Set multi-LLM integration standards with vendor-agnostic design, fallback routing, and cost governance across providers
Own LLMOps at programme scale (eval strategy, prompt governance, observability tooling, safety monitoring, cost controls)




Lead client engineering engagements at senior level (architecture sessions, PoC delivery, alignment with leadership)
Shape and publish reusable patterns, accelerators, and engineering standards for scalable adoption
Own measurement framework for agentic system quality (accuracy, latency, safety, cost) and present AI impact to senior stakeholders
Requisitos principales
Hands-on experience designing and deploying agentic AI solutions in production
Experience with agentic orchestration frameworks at production depth (e.g., LangGraph, CrewAI, AutoGen)
Direct experience calling OpenAI, Anthropic, Vertex AI APIs in production code (provider abstraction, token management, xqbhyrx latency/cost tradeoffs)
RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
LLMOps fundamentals (eval harness design, prompt versioning, production observability)
Cloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC like Terraform/Helm)
Strong Python; Java or equivalent backend language; production debugging/observability
Track record shipping multiple production agentic systems; leadership in people management and performance development
Client-facing leadership
Strategic design thinking
Cross-functional collaboration
Agentic AI
LLM orchestration
RAG and vector databases

📌 AI Native Manager (Málaga)
🏢 Accenture
📍 Málaga