Python állás, munka

• Translate business requirements into technical solutions through workshops, prototyping, and iterative delivery
• Collaborate with customers across the delivery lifecycle (workshops, reviews, UAT) to refine and optimize solutions

Drive Architectural Strategy & Scalability: Partner across Data Science, Platform Engineering, and Product teams to design the technical architecture, data pipelines, and orchestration systems required for high-traffic AI services. Ensure the ecosystem supports highly available, low-latency...

Partner with Marketing: Act as a strategic partner to the Marketing organisation and advocate for data-driven decision-making by effectively communicating AI strategy and its alignment with business objectives....

In Observability you'll build the tools for monitoring and measuring infrastructure, microservices, and sometimes totally unique workloads. You'll put the developer experience at the front of your mind and implementations, and you'll contribute deeply to understanding and preventing incidents...

• Architect complex data flow solutions, enabling transparent, versioned, and fully auditable computations
• Design the data layer by developing metadata and data lineage models fused by semantic graphs and numerical data

Munkakörhöz tartozó feladatok ellátása....

• Design, build, and maintain build a secure and performant unified Edge platform for all partner-facing and customer-facing traffic.
• Bring a strong engineering perspective to the team, helping to instill software design best practices, scalable system architecture, and high-quality code standards.

Design, develop, and maintain AI agents for manufacturing use cases (e.g. quality inspection, anomaly detection, process optimisation), using machine learning, deep learning, GenAI, rules, and reasoning....

Act as a key expert for SAP S/4HANA data migration projects, with particular focus on Material Master and Production Master (I.e., BOMs, Work Centers, Routings and Production Versions)....

You will take a key role in shaping and designing our edge platform for developing, testing, deploying, and operating real-time applications....

• Lead the technical lifecycle of ServiceNow and related governance platforms (IRM, Risk, Change, Incident, CMDB, Asset).
• Ensure configurations are secure, scalable, auditable, and aligned with regulatory standards.

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....

• Own IAM strategy across Entra ID and Active Directory
• Manage conditional access, PIM/PAM, and identity lifecycle governance
• Develop identity-centric detections and threat-hunting workflows, analyzing Entra ID logs and privilege escalation paths in alignment with the SOC

• Build and maintain backend services and APIs using Java and Python
• Work on event-driven data pipelines based on Kafka
• Take ownership of services end-to-end: design, testing, deployment, monitoring, and iteration
• Ensure code quality, including writing and maintaining automated tests

Customer-Focused Outcomes: As a Senior SAP AI Engineer (f/m/d), you will be a key driver of customer value delivery, translating SAP AI architectural blueprints into fully functional, scalable, and production-ready solutions. Specifically, you will:...

You will analyse and evaluate technological developments and market trends in the areas of blockchain, DLT, smart contracts, tokenisation, CBDC and artificial intelligence....

• Developing and evaluating AI systems based on Large Language Models (LLMs), Vision-Language Models (VLMs), and multimodal foundation models
• Designing agentic workflows that automate complex image analysis tasks

• Build Data Pipelines: Design, build, and maintain scalable data pipelines for ingesting, transforming, and serving financial and operational data.
• Work with ERP Data: Integrate and model data from ERP systems and finance tools, including accounting, controlling, procurement, revenue, and cost data.

Analysis of the data ecosystem: assessment of data systems, interfaces and data quality; evaluation of security / privacy and interoperability (data-level to user-level)...

• Keep data moving: Build, run, and refine data pipelines and workflows (Python, Airflow/Cloud Composer) so they're reliable, fast, and easy to change
• Shape our data models: Use BigQueryand Cloud Spanner to design wellstructured, highperformance datasets for analytics and machinelearning use cases