# Senior Analytics & AI Engineer (Associate Director level)
## Overview
We are seeking a hands-on Senior Analytics & AI Engineer at Associate Director level to design, build, and operate production-grade analytics and AI solutions within Global Procurement. This role combines data engineering, BI, ML, GenAI, agentic AI and software engineering to deliver scalable and reliable AI / data products in a modern PowerBI and Databricks environment.
## Key Responsibilities
### Analytics & AI Solution Engineering
- Design, build, and maintain production-grade analytics and AI solutions on Databricks (Python, PySpark, SQL)
- Develop scalable data products supporting procurement analytics, decision intelligence, and AI-driven workflows
- Translate procurement and business requirements into maintainable technical implementations
- Ensure solutions are robust, monitored, scalable, and operationally stable
### BI, Decision Intelligence & Automation
- Build enterprise analytics solutions in Power BI, including semantic models, datasets, and dashboards
- Design reusable data models and KPI logic supporting procurement decision-making
- Enable self-service analytics and standardized reporting across procurement functions
- Drive integration of analytics and AI into operational procurement processes
- Enable automation of analytics and operational workflows using Power Platform (Power Automate)
- Support integration between AI solutions, reporting platforms, and operational procurement processes
- Collaborate with IT and platform teams to ensure alignment with enterprise architecture and governance standards
### GenAI & Agentic AI
- Build GenAI solutions such as contract analysis, supplier intelligence, and procurement copilots
- Apply practical Agentic AI patterns including retrieval, orchestration, evaluation, and workflow automation
- Integrate APIs, tools, and business logic into AI-assisted workflows
### Software Engineering & Engineering Excellence
- Develop high-quality production code using Python, PySpark, and SQL
- Apply modern software engineering best practices including Git-based development, testing and validation, modular and reusable architecture, CI/CD and deployment discipline
- Apply modern AI software engineering practices, including effective use of coding agents and AI-assisted development workflows
- Build and maintain production AI/ML systems with enterprise reliability standards
## Benefits
Generous annual leave, reward plans, flexible working