Job Description
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.