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