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