Lead Ai Engineer

Newcastle, United Kingdom | Posted on 11/09/2026

Scrumconnect Consulting is a multi-award-winning digital consultancy, recognised for delivering impactful and innovative technology solutions across UK government departments. Our work has positively influenced the lives of over 40 million UK citizens. We are passionate about user-centred design, agile delivery, and building digital services that make a real difference - and we're now scaling that expertise into large, high-stakes AI adoption programmes across the public sector.

We're looking for a Lead AI Engineer to be the hands-on technical builder at the core of a large-scale AI Operating Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data.

You'll work inside a collaborative "Rainbow Team" alongside civil servants and the wider delivery team, staying close to the code while also mentoring engineers and helping build the internal capability the department needs to eventually run these systems without long-term reliance on external suppliers.

• Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than delegating it away.
• Technical Leadership Within the Squad Lead the engineering practice within your delivery team: set coding standards, review designs and pull requests, unblock technically complex problems, and mentor other engineers day to day.
• Responsible AI in Practice Implement the guardrails the Architect designs - bias mitigation checks, evaluation harnesses, human-in-the-loop review points - so that Responsible AI principles (ATRS alignment, NCSC "Secure by Design, " meaningful human control) are enforced in the running system, not just on paper.
• Reliability, Security & Observability Build AI services to be secure-by-default and observable in production: logging, monitoring, alerting, and rollback paths appropriate for sensitive public-sector data and high-availability requirements.
• Knowledge Transfer & Capability Uplift Work within the OKUA (Ownership, Knowledge, Understanding, Awareness) framework and "Docs-as-Code" practices to pair with and upskill internal government engineers, so capability genuinely transfers rather than staying locked in the consultancy team.
• Efficient, Sustainable Engineering Favour low-modality, resource-efficient designs where they meet the need - right-sizing models and infrastructure rather than defaulting to the largest/most expensive option - in line with the programme's Green AI and Net Zero commitments.

Loosely mapped to the Government Digital and Data (DDaT) framework, at Lead Engineer level:

• - Coding and Scripting (Expert) - writing production-grade, well-tested code; setting standards for others; comfortable owning components end-to-end.
• - Systems Design (Practitioner) - designing components that integrate cleanly into a wider, architect-defined system; understanding trade-offs across the stack.
• - Data Engineering (Practitioner) - building reliable pipelines to ingest, clean, and prepare data (including unstructured/Legacy sources) for AI consumption.
• - Dev Ops/Continuous Delivery (Practitioner) - CI/CD pipelines, infrastructure-as-code, and automated deployment for AI workloads specifically (not just conventional web services).
• - Testing & Evaluation (Practitioner) - beyond conventional unit/integration testing, building evaluation harnesses for AI system quality: retrieval accuracy, hallucination rate, bias/fairness checks.
• - Problem Solving (Practitioner) - diagnosing and resolving complex, ambiguous technical issues under production pressure.
• - Agile Working (Practitioner) - delivering iteratively within a blended, multidisciplinary team including civil servants.

Given the scale of this programme, direct hands-on depth across the AI engineering stack is essential:

• - Strong general-purpose programming (most commonly Python) applied to AI/ML systems
• - Semantic search, vector/embedding infrastructure, and RAG pipeline construction
• - LLM orchestration and agentic frameworks (eg Lang Chain/Llama…