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• Develop, maintain and optimize Python and Linux shell scripts for data extraction, transformation, and loading.
• Build automated pipelines and reusable code modules to support recurring reporting and analytics workloads.
• Ensure accuracy, completeness, and consistency of datasets.

• Design and build AI agents and workflows that solve concrete business problems
• Take technical ownership of applied AI solutions - from architecture decisions to engineering standards and code quality
• Build enterprise-grade solutions on modern AI platforms, mainly Azure

• Evaluate, configure, and maintain the AI development tool stack (GitHub Copilot, custom agents, local LLMs).
• Standardise how AI agents interact and integrate with our internal tools, services, and development workflows with MCP.

• Write code that is simple, readable, testable, and maintainable, whether you write it yourself or with the help of AI tools
• Conduct code reviews, give helpful feedback, actively contribute in team discussions and decisions

You will become part of our Finance Transformation team within our Technology Consulting practice, supporting local as well as global financial services clients across Banking, Insurance, and Wealth & Asset Management, on finance transformation initiatives and solutions aligned with the CFO...

• Own and continuously develop the Finance systems landscape from a business perspective, ensuring it supports Finance strategy, regulatory requirements, and business growth
• Act as business owner of MS Dynamics 365 F&O subledger environment

Driving and coordinating mission-critical enterprise IT transformation programs, particularly SAP S/4HANA and PLM transformations, with responsibility for program governance, executive reporting, milestone tracking, and cross-functional steering...

You act as a key interface between global HR, regional HR, IT and implementation partners, translating business needs and technical topics into clear HR implications, responsibilities and actions....

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e. g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.

• The data platform end to end: ingestion, storage, and the query paths that keep millions of records fast on screen. You design it, you build it, you run it in production.
• The platform layer, directly with the CTO: infrastructure, deployments, and the path to production for the whole team.

Customer models, end to end. Turn a planning problem, with its capacities, costs, lead times and shift plans, into a model whose answer a plant manager acts on. That includes the data it runs on: ERP and Excel exports, and catching the numbers that cannot be right before the customer does....