AI Architect

Role: AI Architect

Location: London, UK

Employment type: Contract role

As an AI Architect, you will establish the infrastructure, data pipelines, and deployment frameworks necessary to scale Generative AI, predictive machine learning, and multi-agent workflows (Agentic AI) safely and cost-effectively across our global ecosystem.

Key Responsibilities

AI Strategy & System Architecture

• Design Multi-Agent Frameworks: Architect scalable, multi-tenant agentic platforms leveraging Google Cloud’s ecosystem to handle distributed agent workloads, function calling, and asynchronous process orchestration.
• Generative AI & LLM Engineering: Define the blueprint for integrating Gemini Enterprise models, establishing operational boundaries for Retrieval-Augmented Generation (RAG) pipelines, context-window management, and semantic caching.
• Machine Speed Operations: Establish production-grade MLOps pipelines using Vertex AI (including Model Garden, Pipelines, and Feature Store) to govern the complete lifecycle from data ingestion to active model serving.

Platform Infrastructure & Data Engineering

• Cloud-Native Blueprinting: Design secure, scalable microservices architectures leveraging Google Kubernetes Engine (GKE Enterprise), Cloud Run, and event-driven architectures via Pub/Sub.
• Enterprise Data Integration: Architect low-latency, hybrid storage and retrieval mechanisms using BigQuery, Vector Search, and Cloud Storage to support complex vector embeddings and analytical workflows.
• Infrastructure as Code (IaC): Own the automation of the entire AI cloud environment utilizing Terraform, ensuring environment drift is structurally minimized across Dev, Staging, and Prod.

Security, Governance & Compliance

• Guardrails & AI Safety: Implement policy-driven guardrails for AI input/output filtering, managing model hallucination risks, and ensuring explainability metrics align with strict regulatory standards (e.g., FCA Consumer Duty, GDPR).
• Data Isolation & Zero-Trust: Secure multi-tenant SaaS environments using strict GCP IAM structures, Service Accounts, VPC Service Controls, and cryptographic token exchanges.

Technical Requirements & Qualifications

Experience

• Minimum 5+ years of dedicated cloud architecture experience, with at least 2-3 years focused strictly on deploying production-level AI, Machine Learning, or Natural Language Processing (NLP) systems.
• Proven track record of taking a Generative AI application from a prototype (PoC) stage into a scaled, self-healing enterprise production environment.

Core Technical Stack Expertise

• GCP AI Stack: Deep technical mastery of Vertex AI, Gemini API, Dialogflow CX, Agent Assist, and Vertex AI Vector Search.
• Languages & Frameworks: Expert proficiency in Python or R, with hands-on experience in orchestration frameworks such as LangChain, LlamaIndex, or AutoGen.
• DevOps/MLOps Tools: Extensive practice with CI/CD tools, Git, Docker, Kubernetes, and Terraform.