Evaluate, fine-tune, and improve AI systems through structured testing, quality measurement, safety guardrails, and hallucination reduction techniques.
Design and implementation of machine learning models in different domains (e.g. SCM, Manufacturing, Product Management and Corporate Functions) following the CRISP- DM process.
Train, fine-tune, and optimize deep learning models for various applications....
Kubernetes Service Platform Development of managed Kubernetes services with GPU-native orchestration, intelligent scheduling, distributed workload management, cluster lifecycle management, observability, resource isolation, and support for large-scale AI training and inference workloads
AI Model Training and Inference Platform Development of distributed training, model fine-tuning, model deployment, inference acceleration, serverless inference, model APIs, model evaluation, and production-grade LLM and multimodal model-serving capabilities
AI Agent Platform Development of enterprise AI Agent platforms, including Agent building, workflow orchestration, tool integration, retrieval-augmented generation, memory systems, sandbox execution, multi-Agent collaboration, evaluation, observability, security, and large-scale Agent runtime infrastructure...
Responsible to develop User’s Requirement Specification (URS) and Functional Specification (FS)
Driving the collaboration between process development and R&D to promote the simultaneous engineering process for combined product and process development with the aim of generating manufacturable new products in accordance with the CoE’s and division’s needs. Conduct early evaluations of manufacturing technology suppliers in APAC region which would enhance B. Braun manufacturing capabilities, especially in the area of innovative process technologies.
Provide training concepts and execution recommendations for technically skilled workers, to be implemented by a technical training center....