Lead Ai Engineer

Capco; Latina; In loco; EUR 90.000 - 130.000; Tempo pieno

Vantaggi offerti da questo lavoro

Welfare allowance

Access to internal and Tier 1 learning

Descrizione del lavoro

Capco AI Lab is seeking a Lead AI Engineer to design and implement production-grade Generative and Agentic AI solutions for financial services clients. You will guide architecture, set engineering standards, and remain hands-on to deliver robust systems from prototype to production.

Lead client engagements, mentor engineers, and drive technical excellence across projects. The role requires 8+ years in software, strong Python, and deep experience with LLMs, RAG, and agentic frameworks.

Competenze

• 8+ years in Software/Backend Engineering or similar.
• Production-grade Python coding.
• Experience with LLM and Agentic AI applications.
• Strong understanding of RAG, retrieval, vector search.
• Experience with agentic frameworks (LangChain, LangGraph, MCP).
• Testing/monitoring of non-deterministic AI systems.

Mansioni

• 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.
• Engage with clients to translate business requirements into technical architectures and document trade-offs.
• Lead design and code reviews, mentor engineers, raise engineering standards.
• Contribute to technical documentation and thought leadership in Generative and Agentic AI.

Conoscenze

LLM orchestration

AI agents

Python

Cloud platforms

DevOps CI/CD

Distributed systems

Observability

Stakeholder mgmt

Mentoring/leadership

RAG vector search

Descrizione 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.

• 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
• Python or equivalent
• LLM orchestration and AI agents
• RAG architectures and vector-based search