OverviewIn this role you design, build, and deploy production-grade agentic AI systems across enterprise tech stacks.
You work directly with client engineering teams to craft scalable patterns and accelerators that extend beyond single engagements.
The position sits at the core of delivering production AI, with exposure to top tech ecosystems and fellowship programs.
You will shape how multi-agent systems operate in real client environments, from architecture to observability.
ResponsabilitàDesign and build end-to-end production-grade agentic systems (multi-agent orchestration, RAG pipelines, policy routing, tool invocation, memory management, observability)
Own RAG pipelines (embeddings, chunking, vector search, context window tuning)
Integrate across multiple LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) with fallback routing and cost/latency management
Implement LLMOps in production (eval harnesses, prompt versioning, observability tooling, cost and safety monitoring)
Collaborate with client teams to design, prototype, and deploy agentic solutions (workshops, proofs of concept, code-with sessions)
Build reusable patterns, accelerators,

and playbooks for scalable engagements
Define and use metrics for agent accuracy, latency, safety, and cost-effectiveness; present findings to client stakeholders
Requisiti fondamentaliHands-on experience designing and deploying agentic AI solutions in production
Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth
Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code
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 Terraform or Helm)
Strong Python; Java or equivalent backend language; production debugging/observability experience
Track record shipping multiple production agentic systems in four years preferred
client collaboration
problem-solving under pressure
clear technical communication
LangGraph
CrewAI
AutoGen

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