> Markdown version of [/jobs/ext/3274842-engineering-architect](https://www.wearedevelopers.com/jobs/ext/3274842-engineering-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineering Architect - **Company:** YTL Group - **Location:** Bath, UK - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Artificial Intelligence, Applications Architecture, Microsoft Azure, Static Program Analysis, Computer Programming, Design of User Interfaces, Identity and Access Management, Python (Programming Language), Knowledge-Based Systems, Software Systems, TypeScript, Virtualization Technology, Data Logging, Scripting, Deep Learning, Generative AI, Backend, Containerization, Front End Software Development - **Published:** September 16, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a9185b0cae919e95 ## About the Role * deep, current full stack engineering capability with hands-on delivery in an Azure and Azure DevOps (ADO) ecosystem using infrastructure as code approaches * strong practical coding experience in Python and JavaScript/TypeScript, with the fluency to work across back-end services, front-end interfaces and automation scripting * sound stack design judgement, with working knowledge of containerisation, virtualisation and networking sufficient to design, deploy and troubleshoot applications across Azure environments * proven ownership of CI/CD pipelines, infrastructure-as-code, release governance and DORA-based delivery measurement * strong grounding in security (secret management, dependency and static analysis scanning, least-privilege IAM), observability (structured logging, tracing and metrics) and test and evaluation automation * practical experience of AI-first, agentic delivery - rules files, known-good templates, curated skills libraries and evaluation harnesses for AI-generated output * ability to codify standards as machine-readable scaffolding rather than documentation, and to balance light-touch governance against delivery speed * credibility to represent the team's technical position with enterprise architecture, security and assurance functions * experience designing and delivering production-grade AI software systems across different domains * proven expertise in designing and/or implementing generative AI systems in production, including agentic systems, RAG pipelines, tool-use patterns, and model adaptation strategies * practical experience in retrieval and knowledge systems * breadth of knowledge across modern AI technologies, including generative models, deep learning, and predictive ML. ## Description Your focus will be on enabling a fast-moving, AI-first delivery team to consistently deliver high-quality, scalable, and supportable solutions without needing to rebuild core foundations for every delivery cycle. What you'll do In this varied role, you will: * own and version technical foundations across all seven harness dimensions (Security, performance, coding style, UI/UX baselines, logging and observability, testing) so that every deliverable inherits secure, observable and tested defaults * build and maintain the shared ADO CI/CD pipelines and release governance so that standards are enforced automatically, and DORA metrics are produced from the pipeline itself * own the light-touch technical governance model - automated gates over committee review * ensure observability is instrumented from the first commit through to live running so that everything the team ships can be operated and handed over to a receiving product team * contribute hands-on as a full-stack builder inside each delivery's time box so that the harness stays credible and drift is caught in days rather than at cycle end * own the design and hands-on running of the delivery stack end to end - application architecture, containerisation, virtualisation, networking and deployment topology - so that what the team builds can be stood up, scaled and supported in the Azure estate without external dependency * shape deliveries as one of three leads, testing technical feasibility and setting the no-gos, so that committed work stays inside its appetite * represent the team's technical position in assurance, security and enterprise architecture conversations * proactively track industry trends, emerging tools and agentic engineering practice, and self-initiate the learning needed to apply them - trialling promising techniques and approaches in cooldown and sharing what works back into the harness, rules files and skills library.