AI Automation / Application Engineer

As an AI Automation / Application Engineer, you are a hands-on builder reporting to the Digital, Data & AI Infrastructure Lead. You turn operational business challenges into functional, high-impact tools-delivering rapid prototypes, automated data flows, and AI-enabled dashboards that streamline everyday workflows across the organization. Key Responsibilities Here is an overview of the core

responsibilities

AI Agents &

Skills

Build practical AI assistants to handle repetitive tasks, automated document checks, and self-assembling reports. Agentified Dashboards: Develop and deploy R Shiny (or Shiny for Python) applications embedded with natural-language AI co-pilots.

Process Automation: Streamline manual operational processes across spreadsheets, enterprise tools, and business systems. Human-in-the-Loop Workflows: Implement supervised automation patterns requiring human review and approval gates.

Data Pipelines & Scheduled Jobs: Create reliable ETL jobs pulling, cleaning, and reconciling data across systems via REST APIs and SQL. User Adoption & Maintenance: Test prototypes directly with non-technical users, gather feedback, iterate rapidly, and maintain production tools as underlying systems evolve.

Must-Haves Candidates must meet the following minimum criteria: Experience: 3+ years building data applications, process automations, or internal business tooling. Python: Strong scripting, data wrangling (pandas, requests), and automated testing skills.

R & Shiny: Demonstrated ability to build and deploy interactive R Shiny applications. Integration & Data: Proficient in SQL and connecting services via RESTful APIs. Generative AI: Practical experience integrating LLM APIs, prompt engineering, and structured JSON outputs.

Delivery Mindset: Disciplined version control (Git) and ease working iteratively with non-technical end users. Preferred Qualifications The following qualifications are advantageous: Experience with cloud data warehouses (Snowflake) and containerization/cloud deployment (Docker, AWS).

Exposure to agent orchestration frameworks (e.g., LangGraph) or custom tool connectors. Familiarity with pharmaceutical R&D, clinical development, or GxP-regulated workflows. An active GitHub profile or portfolio showcasing live data apps and automations.

Randstad Technologies is acting as an Employment Business in relation to this vacancy...

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