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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Deployment Engineer - **Company:** SkyeBiz - **Location:** London, UK - **Salary:** £59,470.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Applications Architecture, User Authentication, Microsoft Azure, Business Software, Cloud Computing, Information Systems, Databases, Data Integration, Data Structures, Python (Programming Language), Rapid Prototyping Process, Release Management, Software Engineering, Product Software Implementation Methods, Systems Integration, Unstructured Data, Data Logging, Google Cloud, Cloud Platform System, Large Language Models, Prompt Engineering, Software Security, Software Application Programming, Generative AI, Agentic-AI, Information Technology, Api Design, Restful APIs, Data Pipelines, Human in the Loop, Programming Languages - **Published:** October 23, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5938208112 ## About the Role You are likely to be a software engineer, AI engineer, solutions engineer or technical consultant who enjoys working close to customers and solving ambiguous problems. You should have demonstrable experience in: * Building and releasing production software. * Developing applications using LLMs, AI agents or related generative-AI tooling. * Working with APIs, databases and data pipelines. * Integrating multiple business or operational data sources. * Deploying technology into live customer or end-user environments. * Investigating unfamiliar systems and data structures. * Rapidly developing prototypes and iterating from feedback. * Explaining technical decisions to both technical and non-technical audiences. * Taking ownership of loosely defined problems and driving them towards a practical outcome., A degree in Computer Science, Software Engineering, Data Science, Information Systems or a related discipline would be useful. Equivalent practical experience will also be considered, particularly where you can demonstrate that you have personally built, deployed and supported production solutions. Additional experience that would be useful Any of the following would be advantageous, but is not essential: * Forward-deployed engineering. * Solutions engineering or technical consulting. * Professional services delivery. * Customer-facing software implementation. * Technical presales or proof-of-value engagements. * Integration with enterprise business applications. * Cloud, AI/ML or data-engineering certifications. * Working within operationally complex industries. * Experience joining a new product, engineering or delivery capability at an early stage. * Building reusable delivery frameworks from initially bespoke customer engagements. ## Description This is a hands-on role combining software engineering, artificial intelligence, data integration and customer delivery. You will work directly with organisations to understand their operational challenges, identify where AI can create measurable value, and turn those opportunities into secure, production-ready solutions. You will take responsibility for customer engagements from early technical discovery and rapid prototyping through to deployment, adoption and ongoing improvement. Rather than producing demonstrations that remain in a laboratory environment, you will build working solutions that integrate with real data, business applications and operational processes. The position offers significant autonomy and the opportunity to influence how a growing applied-AI delivery capability is structured, documented and scaled. What you will be doing Deliver customer-facing AI solutions * Work directly with customers to understand their systems, data, workflows and commercial priorities. * Convert business challenges into clearly defined technical use cases. * Design, prototype and deploy practical AI-enabled applications. * Establish measurable success criteria for each engagement. * Take solutions from initial concept through testing, production release and user adoption. * Integrate solutions with existing business applications, APIs, databases and data pipelines. * Support customer teams in understanding, operating and extending the delivered solution. Accelerate experimentation and value * Test potential use cases quickly and objectively. * Build working prototypes to validate technical feasibility and business value. * Measure deployed solutions against agreed operational or commercial outcomes. * Use customer feedback and performance data to refine solutions. * Recommend whether an initiative should be expanded, revised or discontinued. * Help stakeholders understand the difference between an interesting AI concept and a viable production solution. Build robust technical foundations * Develop applications using large language models, agent-based approaches, APIs and modern data tooling. * Design integrations across structured and unstructured data sources. * Create reusable components, configuration patterns and implementation assets. * Improve delivery speed and consistency across successive customer engagements. * Produce clear technical documentation to support future maintenance and handover. * Contribute to engineering standards, tooling decisions and deployment practices. Partner with customers and internal teams * Build trusted relationships with technical specialists, operational users and senior decision-makers. * Communicate complex technical concepts in clear commercial language. * Adapt solutions to the way customers operate in practice. * Provide technical support during solution demonstrations, workshops and proof-of-value exercises. * Work closely with engineering, product, commercial and customer-facing teams. * Bring recurring customer requirements and technical lessons back into product development. Maintain quality and responsible AI practices * Ensure solutions are tested for accuracy, reliability and appropriate performance. * Apply proportionate human oversight, validation and monitoring. * Protect confidential customer and company information. * Follow recognised data-protection, information-security and governance principles. * Consider supportability and operational ownership before handing over a deployed solution. * Identify and communicate technical, security and data-quality risks early., * Python or another modern programming language. * Large language models and generative-AI applications. * Agent frameworks and orchestration approaches. * Prompt design, evaluation and testing. * REST APIs and systems integration. * Structured and unstructured data processing. * Data pipelines and transformation. * Cloud environments such as Microsoft Azure, Amazon Web Services or Google Cloud Platform. * Authentication, access control and application security. * Logging, monitoring and production support. * Responsible AI, data privacy and human-in-the-loop controls.