17.09.2025 aktualisiert
100 % verfügbar
AI Software Developer
Nürtingen, Deutschland
Deutschland
MBA in Industrial ManagementSkills
Künstliche IntelligenzAmazon Web ServicesData AnalysisArchitekturGeschäftsplanComputerprogrammierungDatenvisualisierungPerformance ManagementLehrmethodePythonMachine LearningNumpyPredictive AnalyticsProduktdesignVisualisierungPower BiTransformerSentiment DetectionStakeholder ManagementWorkflowsMetrikenFeature-EngineeringData SciencePytorchLarge Language ModelsWirkungsanalyseGenerative AIAws LambdaGitFastapiGeschäftsausrichtungPandasMatplotlibCoaching und MentoringPerformance MonitorDocker
Skills: Artificial Intelligence, Generative AI, Agentic Workflows, Machine Learning, Python, GIT, AWS, Predictive Analytics, Product Development, Docker, FastAPI, Pydantic
Knowledge:
Generative AI & Machine Learning: Expertise in designing, developing, and deploying AI models for training optimization and internal feedback analysis.
Data Science & NLP: Strong experience in data preprocessing, sentiment analysis using BERT, and feature engineering for predictive insights.
End-to-End Model Deployment: Proficient in building and deploying machine learning pipelines, including model fine-tuning, performance monitoring, and result visualization.
Business Alignment & Impact Assessment: Skilled in framing business problems and aligning AI solutions with organizational objectives to drive measurable impact.
Career Highlights:
Designed and deployed a generative AI architecture to enhance corporate training efficiency, successfully improving training outcomes through data-driven insights.
Applied advanced NLP techniques (BERT) for sentiment analysis of training feedback, identifying key themes and emotional tone to improve instructional design.
Developed actionable dashboards and visualizations for training insights, enabling stakeholders to make data-driven decisions on training interventions.
Key Achievements & Impact:
AI Pipeline Development: Built an end-to-end AI pipeline, improving internal training efficiency by providing actionable insights based on structured and unstructured feedback.
Sentiment Analysis with BERT: Implemented sentiment analysis that identified critical factors influencing training success, improving course design and learner outcomes.
Business Impact: Successfully aligned AI-driven solutions with business objectives, leading to improved training efficiency and better allocation of learning resources.
Technical Skills:
Programming & Frameworks: Python, PyTorch, Hugging Face Transformers, BERT, Pandas, NumPy
Machine Learning Techniques: Fine-Tuning Large Language Models (LLMs), Multi-task Learning, Hyperparameter Optimization
Data Preprocessing & Feature Engineering: SQuAD-style dataset creation, Extractive and Abstractive Data Techniques
Model Deployment: AWS Lambda, FastAPI, ROUGE metrics for performance evaluation
Data Visualization: Power BI, Matplotlib
Knowledge:
Generative AI & Machine Learning: Expertise in designing, developing, and deploying AI models for training optimization and internal feedback analysis.
Data Science & NLP: Strong experience in data preprocessing, sentiment analysis using BERT, and feature engineering for predictive insights.
End-to-End Model Deployment: Proficient in building and deploying machine learning pipelines, including model fine-tuning, performance monitoring, and result visualization.
Business Alignment & Impact Assessment: Skilled in framing business problems and aligning AI solutions with organizational objectives to drive measurable impact.
Career Highlights:
Designed and deployed a generative AI architecture to enhance corporate training efficiency, successfully improving training outcomes through data-driven insights.
Applied advanced NLP techniques (BERT) for sentiment analysis of training feedback, identifying key themes and emotional tone to improve instructional design.
Developed actionable dashboards and visualizations for training insights, enabling stakeholders to make data-driven decisions on training interventions.
Key Achievements & Impact:
AI Pipeline Development: Built an end-to-end AI pipeline, improving internal training efficiency by providing actionable insights based on structured and unstructured feedback.
Sentiment Analysis with BERT: Implemented sentiment analysis that identified critical factors influencing training success, improving course design and learner outcomes.
Business Impact: Successfully aligned AI-driven solutions with business objectives, leading to improved training efficiency and better allocation of learning resources.
Technical Skills:
Programming & Frameworks: Python, PyTorch, Hugging Face Transformers, BERT, Pandas, NumPy
Machine Learning Techniques: Fine-Tuning Large Language Models (LLMs), Multi-task Learning, Hyperparameter Optimization
Data Preprocessing & Feature Engineering: SQuAD-style dataset creation, Extractive and Abstractive Data Techniques
Model Deployment: AWS Lambda, FastAPI, ROUGE metrics for performance evaluation
Data Visualization: Power BI, Matplotlib
Sprachen
DeutschgutEnglischverhandlungssicher