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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Technical Architect (AI) - **Company:** scrumconnect ltd - **Location:** Newcastle upon Tyne, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Software Architecture, Search Technologies, Value Engineering, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Multi-Cloud, Generative AI, AI Platforms, Integration Frameworks, Free and Open-Source Software, Machine Learning Operations, Software Version Control - **Published:** September 9, 2026 - **Apply:** https://www.collegerecruiter.com/job/2842870063-lead-technical-architect-ai ## About the Role * AWS / Azure / GCP architecture certifications, AI related certifications most desirable. * Experience contributing to GOV.UK Service Standard alpha or beta assessments * Prior delivery of AI or digital services within UK central government or wider public sector * Familiarity with ATRS submissions, HM Treasury Orange Book, or equivalent public-sector risk/assurance frameworks * Open-source contributions or active engagement in engineering/AI communities * Experience designing for sustainability/Green IT commitments ## Description * Architectural Leadership & Integration Design and implement enterprise-grade AI platforms - including semantic search, Retrieval-Augmented Generation (RAG), and broader generative AI/agentic capabilities - integrating cleanly with the department's complex legacy and multi-cloud environments. * Responsible AI Governance Embed end-to-end responsible AI controls across the full lifecycle: alignment with the Algorithmic Transparency Recording Standard (ATRS), NCSC \"Secure by Design\" principles, and active mitigation of algorithmic bias. Ensure every system operates as a decision-support tool that enforces meaningful human control. * Risk & Value Management Navigate trade-offs between value, risk, pace, and quality using HM Treasury Orange Book and Technology-Organisation-Environment (ToE) frameworks. Identify and resolve systemic risks via Joint Risk Registers. * Zero-Dependency Handover & Capability Uplift Co-deliver within blended agile teams. Drive knowledge transfer using the OKUA (Ownership, Knowledge, Understanding, Awareness) framework and \"Docs-as-Code\" practices so internal staff can independently operate and govern AI capability long after the engagement ends. * Environmental Sustainability (Green AI) Design low-modality, energy-efficient AI architectures aligned with \"Circularity Performance Requirements\" (Buy Better, Use Better, Use Longer), supporting Net Zero 2030/2040 commitments., * Technical Design Throughout the Life Cycle (Expert): technical designs for high-risk, high-impact, high-complexity systems; leading others toward organisational objectives; refining standards from feedback. * Architecture Communication (Expert): communicating complex or contentious architecture to technical and non-technical stakeholders at all levels; mediating difficult discussions; securing executive buy-in. * Making Architectural Decisions (Practitioner): medium-to-high-risk design decisions spanning multiple domains; active contribution to architectural governance and assurance boards. * Architect for the Whole Context (Practitioner): tracking emerging AI and technology trends; influencing colleagues across the organisation to solve or mitigate problems. * Strategy Design (Practitioner): defining architectural principles, AI patterns, and strategic vision aligned to wider government objectives; building implementation roadmaps. * Community Collaboration (Practitioner): proactive networking, resolving team-dynamics issues, using Agile health checks to strengthen the multidisciplinary delivery team. Alongside The DDaT Competencies, Given The Scale Of This Programme, Hands-on Depth Across The Modern AI Engineering Stack Is Essential, Not Just RAG In Isolation * Semantic search, vector/embedding infrastructure, and retrieval architecture design * LLM orchestration and agentic frameworks (e.g. LangChain/LlamaIndex-style tooling, multi-agent patterns) * LLM evaluation, guardrails, and red-teaming tooling (hallucination detection, safety/bias testing) * LLMOps / MLOps: model versioning, deployment pipelines, monitoring, and rollback for AI services in production * Fine-tuning and parameter-efficient adaptation techniques where appropriate to use case * Data engineering for AI: ingestion, cleaning, and lineage across legacy and multi-cloud sources * Infrastructure-as-Code and CI/CD practices adapted for AI workloads, with strong observability * Secure-by-design engineering appropriate to sensitive public-sector data (identity, access control, data protection) ## Related Videos - [The Open-source Java SDK for Multi-Cloud Development - Sandeep Pal](https://www.wearedevelopers.com/videos/2113-the-open-source-java-sdk-for-multi-cloud-development-sandeep-pal) - [This App Reached 10,000 Users in One Week. 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