Use your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency … Experience in DevOps/MLOps practices in deep learning product development
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Job Family - Advanced Analytics & Artificial Intelligence … Technical Skills: Python (preferably fastAPI, SQLAlchemy, pandas, torch) and/or Typescript (preferably React, Next.js, Tailwind CSS), basics of Machine Learning, basics of Docker
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Job Family - Information Technology - Type of employment … Technical Skills: Have experience in NLP/AIML, and MLOps, ideally focusing on Large Language Models (LLMs) as well as in the field of LLMs / Generative AI, Chatbots, RAG, or AI Agents
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Job Family - Research & Development - Type of employment … Explore and apply AI/ML algorithms to enhance automation and efficiency in Electronic Design Automation (EDA) workflows and improve Chip-Package-Board CoDesign methodologies
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Aufbau und Weiterentwicklung lokaler AI-/LLM-Plattformen (On-Prem oder Private Cloud) - Deployment, Fine-Tuning und Optimierung von Open-Source-LLMs (z. B. Llama, Mistral, Qwen) - Planung, Konzeption und Aufbau der erforderlichen Infrastruktur für LLM-Workloads
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ABOUT THE JOB - Accompany clients in delivering business value through AI-driven solutions - Design, develop, and deploy machine learning models to address complex business challenges - Create proof-of-concept ML models from datasets to validate solution feasibility
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Test Strategy & Design: Design, implement, and maintain comprehensive automated test suites specifically tailored for AI/ML systems, covering unit, integration, and end-to-end scenarios. CI/CD Integration: Integrate testing workflows into the Continuous Integration/Continuous Delivery (CI/CD) pipelines, managing …
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Als MLOps Engineer (m/w/d) automatisierst du ML-Pipelines, betreibst und skalierst KI-Plattformen (z. B … Infrastructure-as-Code: Du automatisierst Infrastrukturprozesse und setzt auf Infrastructure-as-Code mit Terraform und Ansible, um Skalierbarkeit sicherzustellen
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Entwicklung und Umsetzung von Machine-Learning-Modellen für Business-Anwendungen - Analyse großer Datenmengen (strukturierte und unstrukturierte Daten) zur Mustererkennung und Prognose - Auswahl und Implementierung geeigneter Algorithmen (z. B
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