Senior AI Engineer in Guildford

Location: Guildford, Surrey (3-4 days onsite per week)

Contract Type: Long-Term Contract (Inside IR35)

Duration: Initial 12 months with strong extension potential

Start Date: ASAP

Overview

We are seeking an experienced AI Solutions Architect / Senior AI Engineer to lead the design, development, and deployment of enterprise‑grade AI solutions that transform content creation, business processes, analytics, and operational workflows. Working closely with senior stakeholders across product, engineering, creative, marketing, data, and operations teams, you will take ownership of AI initiatives from concept through production. This is a hands‑on role requiring deep expertise in modern AI architectures, agentic systems, retrieval‑augmented generation (RAG), knowledge systems, and large language models. The successful candidate will combine strong software engineering and architecture experience with practical AI implementation expertise, helping establish scalable frameworks, governance standards, and reusable AI capabilities across the organisation.

Key Responsibilities

• Design and deliver end‑to‑end AI‑native solutions from proof of concept through to production deployment.
• Architect and implement agent‑based and multi‑agent AI workflows to automate and enhance business processes.
• Evaluate emerging AI technologies and identify opportunities to create measurable business value.
• Define scalable architecture patterns and best practices for enterprise AI adoption.

AI‑Driven Content & Workflow Automation

• Develop AI‑powered tools, assistants, and automation solutions that support content generation, localisation, optimisation, and operational efficiency.
• Build solutions leveraging LLMs, multimodal AI, computer vision, and generative AI technologies.
• Enable teams to automate repetitive tasks and focus on higher‑value strategic activities.
• Design and implement enterprise knowledge systems including:
• Knowledge bases
• Knowledge graphs
• Retrieval‑Augmented Generation (RAG) architectures
• Build robust data ingestion, enrichment, indexing, and retrieval pipelines.
• Define metadata, embedding, and search strategies to maximise AI solution effectiveness.

Production Engineering & Integration

• Productionise AI solutions using modern software engineering practices.
• Integrate AI capabilities with existing enterprise systems, APIs, and platforms.
• Establish monitoring, observability, evaluation, and performance frameworks.
• Support CI/CD, infrastructure automation, and cloud‑native deployment approaches.

AI Governance, Security & Responsible AI

• Implement AI guardrails, governance controls, and evaluation frameworks.
• Ensure compliance with security, privacy, intellectual property, and regulatory requirements.
• Define risk assessment processes, acceptance criteria, and operational standards for AI deployments.
• Establish testing and monitoring strategies to maintain solution quality and reliability.

Stakeholder Collaboration & Leadership

• Partner with technical and non‑technical stakeholders to identify and prioritise AI opportunities.
• Translate business requirements into scalable technical solutions.
• Lead workshops, demonstrations, and technical discussions with senior stakeholders.
• Provide guidance and mentorship on AI engineering best practices and emerging technologies.
• Drive experimentation, prototyping, and iterative development cycles.
• Evaluate new models, frameworks, tooling, and deployment approaches.
• Promote an AI‑first engineering culture and continuous improvement mindset.

Required Skills & Experience

• 8+ years of software engineering, solution architecture, or technical leadership experience.
• 3+ years delivering AI/ML or Generative AI solutions in production environments.
• Experience with modern AI frameworks and orchestration tools such as:
• LangChain
• LangGraph
• Semantic Kernel
• CrewAI
• AutoGen
• Hands‑on experience with:
• LLMs
• AI agents and agentic workflows
• Prompt and context engineering
• Embeddings and vector databases
• Experience designing data pipelines and AI data architectures.
• Knowledge of vector databases such as Pinecone, Weaviate, Chroma, Qdrant, or Azure AI Search.
• Experience with knowledge graph technologies is highly desirable.
• Understanding of model evaluation, benchmarking, observability, and AI monitoring.
• Strong understanding of distributed systems and microservices architectures.
• Experience with one or more major cloud platforms: AWS, GCP.
• Experience with:
• Infrastructure as Code
• API design and integration
• AI Governance & Security
• Experience implementing responsible AI practices and governance frameworks.
• Strong understanding of AI‑related security, privacy, compliance, and IP considerations.
• Ability to define operational guardrails and risk mitigation strategies.

Preferred Experience

• Experience with multimodal AI, computer vision, or image generation technologies.
• Experience fine‑tuning language models or diffusion models.
• Experience with enterprise search and knowledge management platforms.
• Familiarity with AI coding tools such as Cursor, Kiro, Windsurf, Claude Code, GitHub Copilot, or similar.
• Experience working in gaming, media, entertainment, digital content, or customer‑focused industries.