Senior AI Architect

Brillio; Castilla-La Mancha; On-site; EUR 120,000 - 180,000; Full time

Job summary

Brillio is seeking an experienced Senior AI Architect to lead the design, development, and implementation of enterprise-scale AI and Generative AI solutions. You will define AI strategy, architect scalable ML platforms, govern AI best practices, and collaborate with business and technology leaders to deliver secure, production-ready AI capabilities.

The role requires deep expertise in LLMs, MLOps, and cloud architectures; you will mentor teams, drive AI adoption, oversee governance, and lead

Qualifications

• 10+ years in software engineering, data engineering, cloud architecture, or enterprise architecture.
• 5+ years designing and implementing AI and ML solutions.
• Hands-on experience with Generative AI technologies, LLMs, and AI orchestration frameworks.
• Strong expertise in AI architecture patterns, ML lifecycle management, and MLOps practices.
• Experience with enterprise integration, APIs, microservices, and distributed systems.

Responsibilities

• Define and maintain enterprise AI architecture standards, frameworks, and best practices.
• Design and develop end-to-end AI, ML, and Generative AI solutions aligned with business objectives.
• Create scalable architectures for predictive analytics, recommendation systems, and intelligent automation.
• Evaluate emerging AI technologies, frameworks, and vendor solutions to drive innovation.
• Lead AI roadmap development and technology modernization initiatives.
• Architect and deploy Generative AI solutions using LLMs.
• Design Retrieval-Augmented Generation (RAG) architectures and knowledge-based AI systems.
• Implement prompt engineering, agent-based systems, vector databases, and AI orchestration frameworks.
• Develop governance frameworks for responsible AI, explainability, bias mitigation, and security.
• Optimize AI model performance, scalability, and cost efficiency.
• Design AI platforms leveraging cloud services such as Azure, AWS, or Google Cloud.
• Establish AI infrastructure standards including model deployment, monitoring, and lifecycle management.
• Build scalable MLOps capabilities for continuous integration, testing, deployment, and monitoring.
• Implement AI observability, model governance, and performance management practices.
• Partner with executives, business leaders, product teams, and engineering organizations to identify AI opportunities.
• Provide technical leadership to data scientists, ML engineers, data engineers, and software developers.
• Lead architecture reviews, technical design discussions, and AI governance boards.
• Mentor teams and promote AI best practices throughout the organization.
• Ensure AI solutions comply with security, privacy, regulatory, and governance requirements.
• Develop secure AI architecture patterns for handling sensitive data.
• Collaborate with security and compliance teams to mitigate risks associated with AI deployment.
• Establish responsible AI policies and controls.

Skills

AI Architecture

MLOps

Python

Leadership

Education

Bachelor's degree (CS/IT/AI/Data Science)

Master's or PhD

Tools

Azure AI

AWS AI/ML

Google Vertex AI

Kubernetes

Docker

Terraform

Job description

We are seeking an experienced Senior AI Architect to lead the design, development, and implementation of enterprise-scale Artificial Intelligence (AI) and Generative AI solutions. This role is responsible for defining AI strategy, architecting scalable machine learning platforms, governing AI best practices, and collaborating with business and technology leaders to deliver innovative, secure, and production-ready AI capabilities.

The ideal candidate possesses deep expertise in AI/ML architectures, Large Language Models (LLMs), cloud platforms, MLOps, data engineering, and enterprise integration patterns. The Senior AI Architect will serve as a trusted advisor, technical leader, and mentor while driving AI adoption across the organization.

Key Responsibilities

AI Strategy & Architecture

• Define and maintain enterprise AI architecture standards, frameworks, and best practices.
• Design and develop end-to-end AI, Machine Learning, and Generative AI solutions aligned with business objectives.
• Create scalable architectures for predictive analytics, recommendation systems, intelligent automation, and conversational AI.
• Evaluate emerging AI technologies, frameworks, and vendor solutions to drive innovation.
• Lead AI roadmap development and technology modernization initiatives.

Generative AI & LLM Solutions

• Architect and deploy Generative AI solutions using Large Language Models (LLMs).
• Design Retrieval-Augmented Generation (RAG) architectures and knowledge-based AI systems.
• Implement prompt engineering, agent-based systems, vector databases, and AI orchestration frameworks.
• Develop governance frameworks for responsible AI, explainability, bias mitigation, and security.
• Optimize AI model performance, scalability, and cost efficiency.
• Design AI platforms leveraging cloud services such as Azure, AWS, or Google Cloud.
• Establish AI infrastructure standards including model deployment, monitoring, and lifecycle management.
• Build scalable MLOps capabilities for continuous integration, testing, deployment, and monitoring.
• Implement AI observability, model governance, and performance management practices.

Leadership & Stakeholder Engagement

• Partner with executives, business leaders, product teams, and engineering organizations to identify AI opportunities.
• Provide technical leadership to data scientists, machine learning engineers, data engineers, and software developers.
• Lead architecture reviews, technical design discussions, and AI governance boards.
• Mentor teams and promote AI best practices throughout the organization.

Security & Compliance

• Ensure AI solutions comply with security, privacy, regulatory, and governance requirements.
• Develop secure AI architecture patterns for handling sensitive data.
• Collaborate with security and compliance teams to mitigate risks associated with AI deployment.
• Establish responsible AI policies and controls.

Required Qualifications

• Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.
• 10+ years of experience in software engineering, data engineering, cloud architecture, or enterprise architecture.
• 5+ years of experience designing and implementing AI and Machine Learning solutions.
• Hands-on experience with Generative AI technologies, LLMs, and AI orchestration frameworks.
• Strong expertise in AI architecture patterns, machine learning lifecycle management, and MLOps practices.
• Experience with enterprise integration, APIs, microservices, and distributed systems.
• Proficiency with Python and modern AI/ML development frameworks.
• Strong communication, leadership, and stakeholder management skills.

Preferred Qualifications

• Master's or PhD in Artificial Intelligence, Computer Science, Data Science, or related discipline.
• Experience with Microsoft Azure AI, Azure OpenAI, AWS AI/ML, or Google Vertex AI.
• Experience implementing enterprise-scale RAG architectures and AI assistants.
• Knowledge of Responsible AI, AI governance, and ethical AI frameworks.
• Experience with Kubernetes, Docker, Infrastructure as Code (Terraform), and DevOps practices.
• AI, cloud, or enterprise architecture certifications.