20 beste Stellen als python in glonn – aktuell auf Personalsuche

R&D Engineer – AI Integration (m/f/d)
Advantest —München
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- SQL
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Cellebrite —München
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- Azure
- DevOps
- Windows
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cubemos GmbH —München
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- SQL
- AWS
- Agile
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ALTEN Consulting Services GmbH —Poing
- 68.000 € – 72.000 € pro Jahr
- Informatik
- Englisch
- Master
- Weiterbildungsprogramme
- Flexible Arbeitszeiten
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Autodesk —München
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NCTE AG —Oberhaching
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- Mitarbeiter-Rabatt
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Hydrologe (m/w/d)
GICON Großmann Ingenieur Consult GmbH —München
- Vollzeit
- Datenbanken
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Junior Fachspezialist (d/m/w) Hardware & QualitÃ¤tssicherung
genua —Kirchheim bei München
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Associate / Pentester (Red Team) (m/f/d) - Cybersecurity Audit
Siemens —München
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Senior Physicist (m/f/d) - for semiconductor industry
Applied Materials —Heimstetten
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Senior Software Engineer (all genders)
uptodate sustainable solutions GmbH —München
- 60.000 € – 80.000 € pro Jahr
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- AWS
- Künstliche Intelligenz
- Python
- Flexible Arbeitszeiten
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Blackwave GmbH —Taufkirchen
- Vollzeit
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- AWS
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Principal AI Software Engineer (m/f/x) — Industrial AI
appliedAI —München
- Vollzeit
- Azure
- AWS
- APIs
- Homeoffice-Möglichkeit
Direkt bewerben
13 Tage
Experienced Assurance Associate (m/w/d) - Product Owner Digital Solutions
Siemens —München
- Vollzeit
- Hybrides Arbeiten
- Englisch
- Master
- SQL
8 Tage

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python jobs near glonn

R&D Engineer – AI Integration (m/f/d)

Advantest -
München

Jetzt bewerben

Details zur Stelle

Qualifikationen

Azure
Informatik
PyTorch
NumPy
Datenanalyse
Git
Java
Master
Pandas
AWS
C++
Docker
Bachelor
Machine Learning
Computerlinguistik
Linux
Eclipse
Elektrotechnik
Künstliche Intelligenz
Python

Vollständige Stellenbeschreibung

Role Overview:

As an R&D Engineer, you will work on cutting-edge AI and machine learning solutions, with a focus on Large Language Models (LLMs), to enhance semiconductor testing and electrotechnical systems. You will design, implement, and optimize end-to-end AI pipelines, integrating LLM-driven tools into testing workflows and contributing to the development of advanced semiconductor test automation.

Key Responsibilities:

Design, implement, test, and continuously optimize end-to-end RAG (retrieval-augmented generation) pipelines, including data parsing, ingestion, prompt engineering, and chunking strategies
Integrate AI components with existing systems, requiring experience in Java and familiarity with Eclipse
Curate and develop high-quality datasets, including synthetic data generation for robust training and evaluation of LLMs
Preprocess datasets, fine-tune open-source LLMs (e.g., LLaMA, Mistral), and integrate RAG systems into semiconductor testing pipelines
Rigorously evaluate LLM applications on correctness, latency, and hallucination metrics
Assist in deploying LLM-based applications, analyze user feedback, and contribute to iterative improvements
Write clean, maintainable, and testable code following software engineering best practices
Collaborate with cross-functional agile teams to translate customer requirements into prototype solutions, with opportunities to lead smaller sub-projects
Analyze semiconductor testing data (parametric measurements, yield logs) using statistical methods and visualization tools
Contribute to MLOps workflows for model training, evaluation, and deployment using Python frameworks (PyTorch, Hugging Face) and cloud platforms (AWS/Azure)
Integrate AI components with existing systems, requiring experience in Java, C++ and familiarity with Eclipse
Work in Linux environments and handle command-line tools, scripting, and system operations

Requirements:

University degree in Data Science, Computer Science, Electrical Engineering, or a related field (Master's preferred, Bachelor's with 5+ years' experience accepted)
Experience with Advantest V93000 test systems / SmarTest 8
Experience in Eclipse Plugin development
2-4 years of hands-on experience in machine learning, including coursework or practical work with NLP or LLMs
Proficiency in Python for data analysis (Pandas, NumPy) and ML model development (scikit-learn, PyTorch)
Experience programming in Java and C++
Strong understanding of Linux, including command-line usage, system navigation, and scripting
Familiarity with LLM concepts: transformer architectures, prompt engineering, text generation techniques
Foundational knowledge of MLOps practices: version control (Git/DVC), containerization (Docker), and cloud deployment
Experience with Jupyter notebooks / VS Code
Strong communication skills in English; ability to document technical work clearly
Exposure to semiconductor testing data or industrial IoT datasets
Experience with RAG systems, LLM fine-tuning workflows (LoRA, QLoRA)
Familiarity with integrating AI components into existing test platform codebases
Elementary German proficiency

Jetzt bewerben

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