Teams at Intetics

Build production-grade ML and MLOps infrastructure for cloud-native environments. Responsibilities include orchestrating Kubeflow pipelines, training and fine-tuning GPU-based models and LLMs, tracking experiments with MLflow, developing Python tooling, processing large datasets, and maintaining CI/CD, testing, security, and clean-code standards within Kubernetes.

Owns enterprise Azure data platform architecture, including modernization, scalable ingestion, data modeling, warehousing, analytics, governance, and structured/unstructured data processing. Designs implementation-ready solutions using Azure Synapse, Data Factory, Data Lake Storage, Cosmos DB, Databricks, Python, and SQL. Assesses complex environments, establishes architecture standards, optimizes performance and cost, documents target-state architectures, and supports engineering teams through implementation.

Lead change management for SAP S/4HANA transformation programs using ADKAR and Prosci. Develop stakeholder engagement, communication, training, impact assessment, readiness, and adoption strategies across FI, MM, SD, and PP modules. Deliver German-language training and communications, track adoption metrics, manage risks and documentation, collaborate with PMO teams, and support go-live and post-go-live stabilization.