23 set - Palermo
Capco
Overview 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 / Benefits Comprehensive health agevolazioni coverage Meal Vouchers Welfare allowance Access to internal and Tier 1 learning platforms Responsabilità Design 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 components Define engineering standards and best practices for AI apps (reference architectures, testing, guardrails, observability, CI/CD) Architect scalable, secure AI systems considering performance and production requirements Evaluate and select LLMs, frameworks, platforms, and tools Lead design and code reviews, mentor engineers,
raise engineering standards Engage with clients to translate business requirements into technical architectures and document trade-offs Contribute to technical documentation and thought leadership in Generative and Agentic AI Requisiti fondamentali ~8+ years in Software/Backend Engineering or similar ~ Production-grade Python (or equivalent) coding ~ Experience with LLM and Agentic AI applications (agents, orchestration, tool/function calling) ~ Strong understanding of RAG, retrieval, vector search ~ Experience with agentic frameworks (LangChain, LangGraph, MCP) ~ Testing/evaluation of non-deterministic AI systems, monitoring and observability ~ 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 practices ~ DevOps practices including containers, CI/CD, IaC and observability ~ Ability to communicate complex concepts to technical and non-technical stakeholders ~ Fluent professional English ~ strong communication and stakeholder management ~ mentoring and technical leadership ~ client-facing delivery ~ Python or equivalent ~ LLM orchestration and AI agents ~ RAG architectures and vector-based search
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