Forward Deployed Engineer
1 settimana fa
Napoli, Campania, Italia
Accenture
Tempo pieno
Gratuito con email o Google
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Overview In this role you design, build, and deploy production-grade agentic AI systems for enterprise environments. You will collaborate with client engineering teams, lead architecture discussions, and create reusable patterns that scale beyond individual engagements. You’ll own the end-to-end lifecycle of agentic deployments, from orchestration to observability and cost governance. This is a hands-on leadership role shaping large-scale AI capabilities across clients, with direct exposure to senior technology leadership. Responsabilità
* Architect and govern production-grade agentic systems at enterprise scale (multi-agent orchestration, RAG pipelines, memory management, observability)
* Define RAG pipeline standards (chunking, embeddings, quality benchmarks, metrics)
* Set multi-LLM integration standards (vendor-agnostic, routing, cost governance)
* Own LLMOps at programme scale (eval strategy, prompt governance, observability, safety, cost controls)
* Lead client engineering engagements at senior level (architecture design sessions, POC delivery, alignment with leadership)
* Publish reusable patterns, accelerators, and standards for scalable practice
* Own the measurement framework for agentic system quality (accuracy, latency, safety, cost metrics)
* Contribute to production environments with strong software engineering practices Requisiti fondamentali
* Production-level software engineering experience
* Hands-on agentic AI deployments in production (non-negotiable)
* Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth
* Direct experience calling LLM APIs in production (provider abstraction, token management, latency/cost tradeoffs)
* RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
* LLMOps fundamentals (eval harness, prompt versioning, observability)
* Cloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC like Terraform or Helm)
* Strong Python; Java or equivalent backend language; production debugging/observability
* Preferred: track record of shipping three production agentic systems in four years
* People lead
responsibilities:
managing and developing engineering teams, performance management
* leadership
* stakeholder управдение
* communication
* LangGraph
* CrewAI
* AutoGen
* Architect and govern production-grade agentic systems at enterprise scale (multi-agent orchestration, RAG pipelines, memory management, observability)
* Define RAG pipeline standards (chunking, embeddings, quality benchmarks, metrics)
* Set multi-LLM integration standards (vendor-agnostic, routing, cost governance)
* Own LLMOps at programme scale (eval strategy, prompt governance, observability, safety, cost controls)
* Lead client engineering engagements at senior level (architecture design sessions, POC delivery, alignment with leadership)
* Publish reusable patterns, accelerators, and standards for scalable practice
* Own the measurement framework for agentic system quality (accuracy, latency, safety, cost metrics)
* Contribute to production environments with strong software engineering practices Requisiti fondamentali
* Production-level software engineering experience
* Hands-on agentic AI deployments in production (non-negotiable)
* Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth
* Direct experience calling LLM APIs in production (provider abstraction, token management, latency/cost tradeoffs)
* RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
* LLMOps fundamentals (eval harness, prompt versioning, observability)
* Cloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC like Terraform or Helm)
* Strong Python; Java or equivalent backend language; production debugging/observability
* Preferred: track record of shipping three production agentic systems in four years
* People lead
responsibilities:
managing and developing engineering teams, performance management
* leadership
* stakeholder управдение
* communication
* LangGraph
* CrewAI
* AutoGen