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# ai automation/application engineer - python/ r shiny.

- **Company:** Randstad UK
- **Location:** Welwyn Garden City, United Kingdom
- **Experience:** Experienced
- **Salary:** £130,000 - £156,000
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Sql Data Warehouse, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Systems, Spreadsheets, Cloud Computing, Cyber Security, Information Systems, Data Governance, R (Programming Language), Python (Programming Language), Software Deployment, SQL Databases, Systems Integration, AI Infrastructure, Data Processing, Large Language Models, Snowflake, Prompt Engineering, Generative AI, AI Coding Agents, Agentic-AI, Git, Information Technology, Restful APIs, Data Pipelines, Human in the Loop, GXP, Docker
- **Published:** November 10, 2026
- **Apply:** https://www.randstad.co.uk/jobs/ai-automationapplication-engineer-python-r-shiny_welwyn-garden-city_47731319/

## About the Role

* At least 3 years building data applications, automations or internal tools used by business teams.
 * Python: Strong Python for scripting, automation and data processing (pandas, requests, testing).
 * R and Shiny: Solid R and experience building and deploying Shiny applications.
 * SQL and APIs: Good SQL and experience integrating systems through REST APIs.
 * Generative AI: Practical experience building with LLM APIs or AI coding tools, including prompt design and structured outputs.
 * Ways of working: Git and version control; comfortable working iteratively with non-technical users and translating their needs into working tools.
 * You are a pragmatic, hands-on builder who enjoys turning everyday business problems into working tools.
 * You move fast, test with real users and care about code that others can maintain.

Desirable Skills

 * Snowflake or another cloud data warehouse.
 * Building AI agents, agent skills or connectors to business systems.
 * Agent frameworks (e.g. LangGraph).
 * Business intelligence and data-preparation tools.
 * Deploying applications and scheduled jobs with containers (Docker) in a cloud environment (e.g. AWS).
 * Experience in pharmaceutical R&D or another regulated environment (GxP).

Education: Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems or a related field, or equivalent practical experience.

## Description

My global pharma company is looking for an experienced AI Automation/Application Engineer to join their Business Digital, Data & AI Infrastructure sub-function, which designs, builds and runs the data, digital and AI capabilities. You will work closely with business teams, IT & Architecture, Data Governance, Information Security and Quality & Compliance., As an AI Automation / Application Engineer you are a hands-on builder of AI solutions, reporting to the Business Digital, Data & AI Infrastructure Lead. You turn business needs into working tools quickly: AI skills, agentified dashboards, process automations and data pipelines., * AI skills and agents: Build and maintain AI skills and agents that take on repetitive work - for example reports and presentations that assemble themselves, document checks and request-to-system workflows.

 * Agentified dashboards: Develop R Shiny (or Shiny for Python) applications with built-in AI assistants that explain trends, flag issues and answer questions.
 * Process automation: Automate manual, repetitive steps across business tools, documents and spreadsheets.
 * Human-in-the-loop automations: Build supervised automations and agents where people review and approve outputs before they are used.
 * Data pipelines and integrations: Build scheduled jobs that pull, clean, reconcile and combine data from business systems to create reliable insights.
 * Quality and documentation: Write tested, version-controlled code and clear documentation; produce validation evidence for regulated use cases with the Senior AI Solutions Engineer and Quality & Compliance.
 * Adoption and support: Run user testing with business teams, train users, gather feedback, iterate quickly and track usage and time saved.
 * Run and improve: Maintain live tools, fix issues and keep them working as source systems change.