Lead Ai Engineer
Pubblicato il 28-09-2026 - Capco in Firenze
OverviewFaccia clic su "Candidati" qui sotto per inviare la sua candidatura. Si assicuri che il suo CV sia aggiornato e di aver prima letto le specifiche del lavoro.As Lead AI Engineer in Capco AI Lab, you will design and implement production-grade Generative and Agentic AI solutions for financial services clients. You'll guide architecture, set engineering standards, and remain hands-on to deliver robust systems from prototype to production. You'll collaborate with clients and senior stakeholders to translate business needs into scalable AI architectures. This is a senior, client-facing role with impact on technology choices and project delivery, in a growth-focused, inclusive environment.Retribuzione / BenefitsComprehensive health agevolazioni coverageMeal VouchersWelfare allowanceAccess to internal and Tier 1 learning platformsResponsabilitàDesign end-to-end architecture for Generative and Agentic AI solutions (orchestration, AI agents, tool/function calling, RAG, memory/state)Maintain hands-on development, evolving prototypes into production-grade componentsDefine engineering standards and best practices for AI apps (reference architectures, testing, guardrails, observability, CI/CD)Architect scalable, secure AI systems considering performance and production requirementsEvaluate and select LLMs, frameworks, platforms,
and toolsLead design and code reviews, mentor engineers, raise engineering standardsEngage with clients to translate business requirements into technical architectures and document trade-offsContribute to technical documentation and thought leadership in Generative and Agentic AIRequisiti fondamentali8+ years in Software/Backend Engineering or similarProduction-grade Python (or equivalent) codingExperience with LLM and Agentic AI applications (agents, orchestration, tool/function calling)Strong understanding of RAG, retrieval, vector searchExperience with agentic frameworks (LangChain, LangGraph, MCP)Testing/evaluation of non-deterministic AI systems, monitoring and observability xysqume Software architecture fundamentals (APIs, distributed systems, integration patterns, production reliability)Experience with at least one major cloud platform (AWS, Azure, or GCP) and cloud-native practicesDevOps practices including containers, CI/CD, IaC and observabilityAbility to communicate complex concepts to technical and non-technical stakeholdersFluent professional Englishstrong communication and stakeholder managementmentoring and technical leadershipclient-facing deliveryPython or equivalentLLM orchestration and AI agentsRAG architectures and vector-based search