Senior AI Architect

Position: Senior AI Architect|
Job Description Role – AI Evangelist (Senior Technology Architect) Technology – AI/ ML/Gen AI, Data Science, Poly Cloud – Azure, AWS, GCP Location – London – UK Business Unit – TOPAZDLVRYCompensation – Competitive (including bonus)

Job Summary:

We are seeking a highly skilled and experienced Senior Architect/Consultants 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 (Generative Adversarial Networks) and VAEs (Variational Autoencoders). 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 Lang Chain, Lang Graph, Llama Index, 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 & Multi-Agent Architecture:
Deep expertise designing autonomous and multi-agent systems that reason, plan, and act using tools. Command of agentic design patterns (e.g., ReAct, planning, reflection, tool use, human-in-the-loop) and agent frameworks such as Lang Graph, CrewAI, MAF, the OpenAI Agents SDK, and Google’s Agent Development Kit (ADK). Proven ability to architect reliable agentic workflows with memory, state management, orchestration, and safe multi-step task execution at scale.
• Model Context Protocol (MCP) & Interoperability:
Strong working knowledge of the Model Context Protocol (MCP) for standardized, secure connectivity between LLMs/agents and enterprise tools, data sources, and systems. Ability to architect, build, and govern MCP servers and clients and to work with MCP primitives such as tools, resources, and prompts. Awareness of related interoperability standards (e.g., agent-to-agent communication) for composing scalable, enterprise-grade agentic ecosystems.
• Agent Skills & Extensibility:
Experience extending agent capabilities through modular, reusable skills—packaged instructions, scripts, and resources (e.g., SKILL.md-style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools, connectors, and skills that let agents perform specialized, domain-specific tasks reliably, securely, and consistently across teams.
• Retrieval-Augmented Generation (RAG) & Knowledge Architecture:
Expertise architecting RAG and knowledge-grounded systems—chunking strategies, embeddings, vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector, FAISS), hybrid search, reranking, and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.
• LLMOps, Evaluation & Responsible AI:
Experience operationalizing LLM and agentic systems at scale—evaluation harnesses and metrics for quality, groundedness, and safety; observability, tracing, and monitoring (e.g., Lang Smith, Lang Fuse); 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…