AI Architect

Your Role As an AI Architect, you will own the architecture and industrialization of AI capabilities across the full spectrum from Generative AI and Agentic AI to Computer Vision, local and cloud models, and AI-enabled business processes. You will be the connective tissue between business requirements, data, models, infrastructure, security and operations ensuring every AI solution is measurable, governed, secure and production-ready. In this role you will play a key role in: • Defining target architectures for LLM, RAG, Agentic AI, Computer Vision and multimodal capabilities, including agent design, memory, function calling, human-in-the-loop controls and multi-agent patterns • Architecting and implementing AI and business-process orchestration solutions using platforms such as Langflow and Flowable, translating complex business processes into executable workflows that integrate people, AI agents, APIs and enterprise systems using BPMN 2.0 • Establishing and governing the AI evaluation strategy, including Golden Dataset methodology, task-specific metrics, benchmarking, regression evaluation and maintaining clear separation between training and evaluation data • Defining production observability frameworks covering drift detection, quality degradation monitoring, feedback loops and continuous re-evaluation of approved AI solutions • Designing local and sovereign model architectures using Ollama, vLLM and the Hugging Face ecosystem, evaluating quality, security, sovereignty, cost and operational trade-offs between local and managed models • Defining RAG pipelines including ingestion, chunking, embedding, vector search, hybrid retrieval and reranking, as well as Computer Vision pipelines for classification, detection, segmentation and anomaly detection • Governing the full MLOps and LLMOps lifecycle from development and evaluation through to deployment, observation and continuous improvement while embedding security, Responsible AI principles and architectural governance throughout • • Proven experience as an AI Architect or senior technical architect with 8 12 years of hands-on experience across GenAI, LLM, RAG and Agentic AI, with practical application of IAF, TOGAF or an equivalent recognised architecture framework • Strong command of AI engineering fundamentals including Python, REST APIs, vector databases, GPU/inference concepts, containerisation and cloud AI platforms, with experience governing MLOps and LLMOps pipelines end-to-end • Demonstrated ability to define and operationalise AI evaluation strategies, including Golden Dataset design, model benchmarking, drift detection and observability balancing AI autonomy with deterministic controls and human accountability • Experience architecting AI and business-process orchestration solutions using tools such as Langflow, LangChain, LangGraph or Flowable, with solid understanding of BPMN 2.0 and the ability to translate business processes into governed, scalable AI-integrated workflows • Strong stakeholder communication skills with the ability to challenge model and platform choices using evidence-based evaluation, and to influence both technical teams and senior business leaders • Experience in security and Responsible AI by design, including secure model endpoints, guardrails, controlled tool access and appropriate explainability practices