AI ENGINEERING ARCHITECT- ITALY (REMOTE)

Role Overview
Our Client is looking for a highly skilled AI Engineering Architect to lead the architecture, engineering, and deployment of scalable, secure, and production-ready Artificial Intelligence solutions.
The successful candidate will combine strong software engineering and cloud architecture expertise with deep knowledge of Generative AI, Large Language Models (LLMs), Machine Learning, AI Agents, RAG, MLOps/LLMOps, and AI platforms.

Key Responsibilities

1. AI Architecture & Technical Leadership

Design and own end-to-end architectures for AI, Machine Learning, Generative AI, and Agentic AI solutions.
Define scalable, reliable, secure, and cost-efficient AI architecture patterns and technical standards.
Translate business and product requirements into technical AI architecture and implementation strategies.
Develop architecture diagrams, technical specifications, design principles, and reference architectures.
Provide technical leadership to AI engineers, software engineers, data scientists, and data engineers.

1. Generative AI & LLM Engineering

Design and implement production-grade solutions using LLMs and Generative AI.
Design and deploy AI agents and multi-agent systems for enterprise workflows.
Develop strategies for prompt engineering, model evaluation, model routing, and LLM orchestration.
Evaluate and integrate models from providers such as OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, Anthropic, and open-source models.
Establish multi-LLM and vendor-agnostic architecture where appropriate.
Design mechanisms for model fallback, performance optimisation, latency management, and cost control.

1. AI Engineering & Software Development

Design APIs, microservices, event-driven systems, and integrations supporting AI applications.
Build reusable AI components, frameworks, and services that can be adopted across multiple products.
Collaborate with software engineering teams to integrate AI capabilities into existing platforms and applications.
Review code and establish engineering best practices for AI development.

1. Cloud & Infrastructure Architecture

Design and implement AI workloads on AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Architect scalable cloud infrastructure for model training, inference, data processing, and AI applications.

1. MLOps / LLMOps

Establish best practices for the development, deployment, monitoring, and lifecycle management of AI models.
Design MLOps and LLMOps pipelines for continuous integration, testing, evaluation, deployment, and monitoring.
Implement model versioning, experiment tracking, automated evaluation, and performance monitoring.
Establish monitoring for model quality, hallucination, latency, usage, cost, and reliability.

1. AI Security, Governance & Responsible AI

Implement appropriate controls around authentication, authorization, data access, model access, and sensitive information.
Support compliance with applicable EU and Spanish AI, data protection, and cybersecurity requirements.
Ensure AI solutions are designed with appropriate human oversight, monitoring, and safeguards.

1. Innovation & Technology Evaluation

Research and evaluate emerging AI technologies, models, frameworks, and infrastructure.
Assess new LLMs, agentic frameworks, vector databases, AI development platforms, and AI engineering tools.
Conduct technical PoCs and determine their suitability for production environments.
Define the organisation's AI technology roadmap and recommend improvements to the AI engineering stack.

Required Qualifications & Experience
Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Engineering, or a related field.
7+ years of experience in software engineering, solution architecture, AI/ML engineering, or related technical roles.
3+ years of hands-on experience designing and implementing AI/ML or Generative AI solutions.
Proven experience designing and deploying production-grade AI systems.
Strong experience with Generative AI, LLMs, RAG, AI agents, and model integration.
Experience with at least one major cloud platform: AWS, Azure, or GCP.
Strong understanding of MLOps, LLMOps, model lifecycle management, and AI observability.
Experience with relational and/or Strong understanding of data pipelines, data processing, embeddings, and information retrieval.
Experience with AI security, governance, privacy, and responsible AI principles.
Strong problem-solving, architecture, communication, and technical leadership skills.

Preferred Qualifications
Master's degree or PhD in AI, Computer Science, Machine Learning, or a related discipline.
Experience building AI agents or multi-agent systems in production.
Experience with enterprise GenAI platforms or AI Centers of Excellence.
Experience designing multi-LLM architectures.