AI Architect (GenAI & Agentic Systems): Contract (12 Months) £1,000–£1,200/day

Location: UK / Hybrid (London preferred)

Role Overview

We are seeking a hands-on AI Architect to design, build, and deploy production-grade Generative AI and Agentic AI systems across complex enterprise environments, particularly within Financial Services, Banking, and Telecommunications.

This is not a research role. You will have delivered real AI systems on the ground, ntegrating Large Language Models (LLMs) into enterprise platforms, orchestrating autonomous agents, and operating within strict regulatory, security, and reliability constraints.

You will define architecture, select models, design agent frameworks, and lead implementation from proof-of-value through to scaled production.

Key Responsibilities

• Architect end-to-end GenAI and Agentic AI solutions for enterprise use cases

• Design systems integrating LLMs into AI agents, workflows, and applications

• Lead selection and implementation of models (OpenAI, Anthropic, open-source, domain-tuned)

• Build multi-agent orchestration frameworks and tool-using agents

• Implement Retrieval-Augmented Generation (RAG) architectures at scale

• Design secure deployment patterns (cloud, on-prem, hybrid)

• Integrate AI services with enterprise data platforms, APIs, and legacy systems

• Define guardrails, governance, explainability, and safety controls

• Optimize performance, latency, and cost at scale

• Establish MLOps / LLMOps pipelines and monitoring frameworks

• Provide technical leadership to engineering and data teams

• Translate business requirements into deployable AI capabilities

Core Technical Expertise Required

LLMs & Generative AI

• Deep experience integrating production LLMs into applications

• Prompt engineering, tool use, function calling, reasoning chains

• Model evaluation, benchmarking, and tuning

• Fine-tuning, adapters, or domain specialization (LoRA, PEFT etc.)

Agentic AI Systems

• Design and deployment of autonomous or semi-autonomous AI agents

• Multi-agent collaboration patterns

• Planning, memory, tool orchestration

• Human-in-the-loop controls

RAG & Knowledge Systems

• Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)

• Embeddings pipelines and semantic search

• Knowledge grounding and hallucination mitigation

Architecture & Platforms

• Cloud: AWS / Azure / GCP (enterprise-scale deployments)

• Containerization: Docker / Kubernetes

• Microservices and event-driven architectures

• API design and integration

LLMOps / MLOps

• Model deployment pipelines

• Observability, drift monitoring, evaluation frameworks

• Cost management and scaling strategies

Industry Experience (Highly Desirable)

• Financial Services / Banking / Insurance

• Telecommunications

• Regulated enterprise environments

• Data privacy and security-critical systems

Essential Background

• Proven track record delivering AI systems into production, not just prototypes

• Experience working with C-suite stakeholders and enterprise architects

• Strong software engineering foundation

• Ability to operate in ambiguous, fast-moving programmes

• Excellent communication and leadership skills

Preferred Experience

• Building enterprise copilots or digital workforce solutions

• Conversational AI at scale

• Integration with core banking or telecom platforms

• Knowledge of regulatory frameworks (GDPR, model risk, etc.)

• Experience with both proprietary and open-source LLM stacks

Key Deliverables

• Enterprise-ready AI architecture and roadmap

• Production deployment of agentic AI solutions

• Scalable, secure GenAI platform components

• Govern