AI Native Software Engineering Specialist
bOverview /b pIn 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. /p bResponsabilità /bulliDesign and build end-to-end production-grade agentic systems (multi-agent orchestration, RAG pipelines, policy routing, tool invocation, memory management, observability) /liliOwn RAG pipelines (embeddings, chunking, vector search, context window tuning) /liliIntegrate across multiple LLM providers (OpenAI, Anthropic, Vertex AI, open-source models) with fallback routing and cost/latency management /liliImplement LLMOps in production (eval harnesses, prompt versioning, observability tooling, cost and safety monitoring) /liliCollaborate with client teams to design, prototype, and deploy agentic solutions (workshops, proofs of concept, code-with sessions) /liliBuild reusable patterns, accelerators,
and playbooks for scalable engagements /liliDefine and use metrics for agent accuracy, latency, safety, and cost-effectiveness; present findings to client stakeholders /li /ul bRequisiti fondamentali /bulliHands-on experience designing and deploying agentic AI solutions in production /liliExperience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth /liliDirect experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code /liliRAG pipeline ownership (embeddings, chunking, vector databases, context engineering) /liliLLMOps fundamentals (eval harness design, prompt versioning, production observability) /liliCloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC Terraform or Helm) /liliStrong Python; Java or equivalent backend language; production debugging/observability experience /liliTrack record shipping multiple production agentic systems in four years preferred /li /ululliclient collaboration /liliproblem-solving under pressure /liliclear technical communication /liliLangGraph /liliCrewAI /liliAutoGen /li /ul
and playbooks for scalable engagements /liliDefine and use metrics for agent accuracy, latency, safety, and cost-effectiveness; present findings to client stakeholders /li /ul bRequisiti fondamentali /bulliHands-on experience designing and deploying agentic AI solutions in production /liliExperience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen) at production depth /liliDirect experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code /liliRAG pipeline ownership (embeddings, chunking, vector databases, context engineering) /liliLLMOps fundamentals (eval harness design, prompt versioning, production observability) /liliCloud-native engineering maturity (Kubernetes, Docker, microservices, CI/CD, IaC Terraform or Helm) /liliStrong Python; Java or equivalent backend language; production debugging/observability experience /liliTrack record shipping multiple production agentic systems in four years preferred /li /ululliclient collaboration /liliproblem-solving under pressure /liliclear technical communication /liliLangGraph /liliCrewAI /liliAutoGen /li /ul