OverviewIn this role you design, build, and deploy production-grade agentic AI systems across enterprise stacks, working directly with client engineering teams.
You'll lead technical design, publish reusable patterns, and ensure scalable, observable AI solutions that run in client environments.
You shape the tooling and governance around RAG pipelines, multi-LLM integration, and LLMOps across programs.
This is a hands-on position with significant client impact and opportunities to collaborate with leading AI engineering teams.
Retribuzione / Benefitsvendor fellowship access inside Anthropic, OpenAI, Microsoft and Google engineering teams
Forward Deployed Engineer programme
broad cross-industry exposure
career development opportunities
global delivery capability
competitive compensation depending on role and seniority
ResponsabilitàArchitect and govern production-grade agentic systems at enterprise scale including multi-agent orchestration, RAG pipelines, memory management, and lifecycle observability
Define RAG pipeline standards: chunking, embeddings, quality benchmarks, measurable trade-offs
Set multi-LLM integration standards with vendor-agnostic design and cost governance
Own LLMOps at program scale: eval strategy, prompt governance, observability, safety, and cost controls
Lead client engineering engagements at senior level: design sessions, POC delivery, align client leadership with delivery
Shape and publish reusable patterns, accelerators, and standards to scale engagements
Own the measurement framework for agentic system quality: define metrics and present AI impact to senior stakeholders




Demonstrate production software engineering experience and hands-on deployment of agentic AI solutions
Demonstrated experience with agentic orchestration frameworks in production (LangGraph, CrewAI, AutoGen, or equivalent)
Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production and managing latency/cost tradeoffs
RAG pipeline ownership: embeddings, chunking, vector databases, context engineering
LLMOps fundamentals: eval harness design, prompt versioning, production observability
Cloud-native maturity: Kubernetes, Docker, microservices, CI/CD, IaC (Terraform or Helm)
Strong Python; Java or equivalent backend language; production debugging and observability
Experience leading teams of engineers and mentoring talent
Ability to ship three production agentic systems in four years is preferred over broad AI exposure
Requisiti fondamentaliproduction-level software engineering experience
hands-on experience deploying agentic AI solutions in production
experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen, or equivalent) in production
experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production
RAG pipeline ownership (embeddings, chunking, vector databases)
LLMOps fundamentals (eval harness, prompt versioning, observability)
cloud-native engineering (Kubernetes, Docker, microservices, CI/CD, IaC)
strong Python; Java or equivalent backend language
people leadership: managing and developing a team of engineers
leadership
client-facing communication
architecture design facilitation
LangGraph
CrewAI
AutoGen

📌 Ai Native Associate Director (Napoli)
🏢 Accenture
📍 Napoli