Lead Ai Engineer

Capco; Trieste/Trst; In loco; EUR 90.000 - 130.000; Tempo pieno

Vantaggi offerti da questo lavoro

Welfare allowance

Access to internal learning platforms

Tier 1 learning platforms

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.

You will collaborate with clients and senior stakeholders to translate business needs into scalable AI architectures. This senior, client-facing role demands strong leadership, deep AI engineering experience, and

Competenze

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

Conoscenze

Python

LLM orchestration

Cloud platforms

DevOps

API design

Mentoring

Stakeholder management

English communication

Strumenti

LangChain

LangGraph

MCP

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