Lead AI Architecture for Enterprise Automation

GBG

Chester

On-site

GBP 90,000 - 130,000

Full time

4 days ago

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Job summary

GBG is seeking a Lead AI Technical Architect to steer GBG’s AI and automation transformation. You will translate business opportunities into scalable technical solutions, define reusable patterns, and provide hands-on engineering leadership to the central AI & Automation team.

The role combines architectural strategy with direct delivery, ensuring secure, scalable and measurable outcomes. You will collaborate across Technology, Data and Infosec to align with GBG’s standards and drive impactful

Qualifications

Enterprise AI and automation solution design and delivery.
Architecture across AI, workflow automation, APIs and cloud apps.
Hands-on AI with LLMs, RAG, AI-assisted workflows or agentic capabilities.
Translate business priorities into practical technical solutions.
Delivery-focused with momentum and sustainable quality.
Experience integrating enterprise automation across systems.
Strong engineering, testing, deployment, monitoring and support.
Lead technical teams and provide architectural direction.
Communicate complex concepts to non-technical stakeholders.
Hands-on with Azure, Power Platform, Copilot Studio, Python and .NET.
Foundry/Azure AI Studio desirable.

Responsibilities

Lead architecture and solution design for AI and automation initiatives across GBG.
Translate business requirements into practical, deliverable technical solutions.
Establish reusable patterns and integration approaches for multiple functions.
Provide technical leadership to the AI & Automation team, including staff and contractors.
Own technical estimates, quality, design decisions and delivery readiness.
Stay involved in delivery through prototyping, reviews and debugging.
Collaborate with project manager to plan delivery and milestones.
Partner with SMEs to validate designs and ensure outcomes.
Work with tech, data and infosec to meet governance and security requirements.
Establish testing, reliability, monitoring and support standards.
Evaluate platforms and promote reuse to avoid excess cost.
Ensure production deployment with proper documentation and ownership.
Address dependencies and enabling capabilities for future work.
Develop engineering capability through coaching and knowledge sharing.
Measure solution performance and business impact.