Lead AI Engineer

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 benefit 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