Senior AI Architect| London

We are seeking a highly skilled and experienced Senior Architect/Consultant to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI, LLMs, retrieval‑augmented generation (RAG), machine learning, and modern interoperability standards such as the Model Context Protocol (MCP), along with a proven track record of architecting and implementing innovative, enterprise‑scale solutions. As a Senior Architect/Consultant, you will play a pivotal role in shaping our Generative AI strategy, selecting appropriate models and technologies, and collaborating with cross‑functional teams to deliver cutting‑edge solutions that meet customer requirements and business objectives.

Primary Skill Set

• Generative AI Expertise: In-depth knowledge of modern Generative AI techniques and foundation models, including transformer‑based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs. Experience across text, code, image, and multimodal generation is essential. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands‑on exposure to both API‑based (e.g., Claude, GPT, Gemini) and open‑source (e.g., Llama, Mistral) LLM‑based solution design. Agentic AI observability, tracing, and monitoring (e.g., LangSmith, LangFuse); guardrails and red‑teaming; and continuous optimization of accuracy, cost, and latency. Understanding of AI governance, security, privacy, bias/fairness, and emerging AI regulation.
• Machine Learning Mastery: Profound understanding of machine learning principles, algorithms, and frameworks. Able to design and implement models, optimize performance, and manage training pipelines effectively.
• Technical Proficiency: Proficiency in programming languages commonly used in AI development, such as Python, TensorFlow, PyTorch, or similar tools, along with modern LLM/agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen). Experience with cloud AI platforms (e.g., Amazon Bedrock, Azure OpenAI / AI Foundry, Google Vertex AI), vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), containerization and orchestration (Docker, Kubernetes), and distributed computing is advantageous.
• Architecture Design: Ability to design end‑to‑end Generative and Agentic AI architectures that encompass data preprocessing, model selection, RAG pipelines, agent orchestration, MCP‑based tool and system integration, guardrails, training/inference pipelines, and deployment strategies. Strong grasp of scalable, reliable, secure, and cost‑ and latency‑efficient system design for enterprise‑grade AI.

Secondary Skill Set

• Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g., healthcare, finance, entertainment) is beneficial. This enables contextual understanding and tailored solution development.
• Data Engineering: Understanding of data engineering practices, data pipelines, and data management. Proficiency in data preprocessing, cleansing, and transformation for effective model training.