STAAI ArchitectLondon

Role AI Evangelist (Senior Technology Architect)
Technology AI/ ML/Gen AI Data Science Poly Cloud Azure AWS GCP
Location London UK
Business Unit TOPAZDLVRY
Compensation 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 Googles 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.

-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 models optimize performance and manage training pipelines effectively.
Technical Proficiency: Proficiency in programming languages commonly used in AI development such as Python Tensor Flow PyTorch or similar tools along with modern LLM/agent frameworks (Lang Chain Lang Graph Llama Index Semantic Kernel CrewAI Auto Gen).

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.

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