Overview In this role you build production-grade agentic AI systems that scale across enterprise environments. You’ll work directly with client engineering teams to design, deploy, and govern multi-agent architectures and reusable patterns. The position sits at the core of Accenture’s AI practice, offering breadth across industries and direct access to leading engineering ecosystems. You’ll tackle real-world reliability, latency, and cost tradeoffs, shaping impactful AI deployments. This is an opportunity to lead the design and delivery of mission-critical AI systems at scale.

Compensaciones / Beneficios

vendor fellowship access inside leading AI companies Forward Deployed Engineer programme broad industry exposure career development opportunities general delivery capability

Responsabilidades

Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration, RAG pipelines, policy routing, memory management, and observability Define RAG pipeline standards: chunking, embeddings, quality benchmarks, and metric-backed decisions Set multi-LLM integration standards with vendor-agnostic architecture and cost governance across providers Own LLMOps at programme scale: eval strategy, prompt governance, observability, safety monitoring, and cost controls Lead client engineering engagements at senior level: architecture sessions, PoC delivery,



aligning client leadership and delivery Shape and publish reusable patterns, accelerators, and engineering standards for scalable engagement ramps Own measurement framework for agentic system quality: metrics for AI impact and business value to stakeholders

Requisitos principales

Software engineering experience in production environments Hands-on production experience with agentic AI solutions Experience with agentic orchestration frameworks at production depth (e.g., LangGraph, CrewAI, AutoGen) Direct production experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) with provider abstraction, token management, latency and 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, serverless, CI/CD, IaC (Terraform or Helm) Strong Python; Java or equivalent backend language; production debugging and observability People leadership: managing and developing a team of engineers; performance management Track record of shipping three production agentic systems in four years preferred leadership communication with client stakeholders architecture facilitation LangGraph CrewAI AutoGen

📌 Forward Deployed Engineer - sevilla
🏢 Accenture
📍 Sevilla