Lead AI Engineer
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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 benefit coverage
- Meal Vouchers
- Welfare allowance
- Access to internal and Tier 1 learning platforms
- 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
- 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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