Lead AI Architect
HybridMadrid, Madrid, Spain or Glasgow, Scotland, United Kingdom
Job Summary
Architect and own enterprise-scale Generative AI platforms supporting multiple business use cases, including internal pilots, automation assistants, and customer-facing AI experiences. Define and maintain reference target-state data and AI architectures for International Markets, ensuring consistency, reuse, and long-term sustainability across the landscape. Act as a senior architectural authority for AI-enabled data solutions, embedding modern AI and analytics patterns into enterprise data platforms while advocating for responsible adoption. Collaborate closely with business leaders, product teams, and engineering to drive innovation and operational improvements, translating requirements into coherent end-to-end solution designs. Participate across the full delivery lifecycle from concept shaping through design assurance, capturing and managing architectural risks and assumptions. Provide high-level mentoring to teams to embed data and AI principles, while taking a lead role in selecting and assessing third-party data and AI solutions. Maintain an active awareness of emerging trends, advising senior leadership on strategy, feasibility, and adoption paths within regulated healthcare environments.
Required Qualifications
- Advanced degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
- 10+ years of experience building and architecting large-scale software or AI systems in medium-to-large enterprises
- 5+ years of experience with AI/ML, NLP, or Generative AI
- Experience working in regulated or compliance-sensitive environments (e.g., healthcare, finance, insurance, global enterprises)
- Strong communication skills with the ability to influence technical and non-technical stakeholders, with English at C2 level
- Demonstrated expertise in architecting and leading large-scale AI initiatives, taking solutions from proof of concept through to successful production deployment
- Deep understanding of architectural considerations and trade-offs in AI and machine learning projects, built through hands-on delivery experience
- Experience working effectively within globally distributed teams and complex stakeholder environments
- Knowledge on foundational models and inference profiles, regional hosting and restrictions of various models and be able to identify the right model for the right requirements
- Proficiency in designing and implementing AI Gateway/model access layers to facilitate secure and scalable access to AI models
- Experience in architecting solutions with vector databases and semantic search for efficient retrieval of information from large volumes of unstructured data
- Skilled in leveraging document intelligence platforms (e.g., Azure Form Recognizer, AWS Textract, Google Document AI) for document classification, data extraction, and entity recognition from varied formats including scanned documents, PDFs, and handwritten forms
- Knowledge of Intelligent Document Processing (IDP) solutions for automated ingestion, parsing, and validation of unstructured documents, ensuring accurate routing of documents to appropriate business workflows
- Familiarity with workflow orchestration frameworks (such as LangChain, LangGraph, or equivalent) to automate end-to-end document processing pipelines, including document routing, exception handling, and integration with downstream systems
- Understanding of optical character recognition (OCR), natural language processing (NLP) techniques, and entity linking for extracting actionable insights from diverse data types
- Awareness of data validation, quality checks, and document lifecycle management best practices to ensure compliance and reliability in document processing solutions
- Familiarity with AI governance, model lifecycle considerations, and responsible use of data and AI, particularly within regulated environments
Desired Qualifications
- Generative AI & Core Concepts: Large Language Models (LLMs), embeddings, prompt engineering - Retrieval-Augmented Generation (RAG), Knowledge bases and Multimodal embeddings
- Agentic AI workflows and tool calling - Model Context Protocol (MCP) concepts and context management
- Awareness of AI Gateway / model access layer design and access controls on the gateways
- Experience in architecting solutions with vector databases and semantic search for efficient retrieval of information from large volumes of unstructured data
- Skilled in leveraging document intelligence platforms (e.g., Azure Form Recognizer, AWS Textract, Google Document AI) for document classification, data extraction, and entity recognition from varied formats including scanned documents, PDFs, and handwritten forms
- Knowledge of Intelligent Document Processing (IDP) solutions for automated ingestion, parsing, and validation of unstructured documents, ensuring accurate routing of documents to appropriate business workflows
- Familiarity with workflow orchestration frameworks (such as LangChain, LangGraph, or equivalent) to automate end-to-end document processing pipelines, including document routing, exception handling, and integration with downstream systems
- Understanding of optical character recognition (OCR), natural language processing (NLP) techniques, and entity linking for extracting actionable insights from diverse data types
- Awareness of data validation, quality checks, and document lifecycle management best practices to ensure compliance and reliability in document processing solutions
- Familiarity with AI governance, model lifecycle considerations, and responsible use of data and AI, particularly within regulated environments
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