28.11.2025 aktualisiert
100 % verfügbar
AI Engineer - Data Engineering & Machine Learning Specialist
Ingolstadt, Deutschland Master of Science in Mathematical Modelling, Simulation, and Optimization
Skills
JavascriptKünstliche IntelligenzData AnalysisUnit TestingAutomobilindustrieMicrosoft AzureComputational Fluid DynamicsCloud ComputingCloud-SpeicherDatenbankenInformation EngineeringData GovernanceETLProgrammierwerkzeugeWeb EntwicklungForecastingRohreGithubWärmeübertragungPythonPostgresqlMachine LearningMathematische ModellierungMonte-Carlo-SimulationGießereiNosqlNumerische AnalysePredictive ModellingQualitätsmanagementAzure Machine LearningSemantikSoftwareentwicklungSQLTypescriptMicrosoft Power AutomateReactJSFlaskLarge Language ModelsPrompt EngineeringFacebook FlowGenerative AIGitFastapiIntegrationstestsKubernetesAzure AKSRestful ApisSicherheitsbestimmungenGPTDaten-PipelineApi-ManagementServerless ComputingDockerMicroservices
AI & Machine Learning
Generative AI, Large Language Models (GPT-4, GPT-4o), Agentic AI Systems, Retrieval-Augmented Generation (RAG), Predictive Modeling, Forecasting Models, NLP Pipelines, Embedding Models, Semantic Search, Vector Databases, Model Evaluation & Optimization, Prompt Engineering, Chain-of-Thought Orchestration, Model Context Protocol (MCP)
AI Platforms & Cloud
Azure AI Foundry, Azure Machine Learning, Azure Cognitive Search, Azure Functions, Azure App Service, Azure Kubernetes Service (AKS), LLMaaS (VQ), Cloud-Native AI Deployment, API Management
Software Engineering
Python, FastAPI, Flask, REST API Development, JavaScript/TypeScript, React, Web Development, Microservices Architecture, Docker, Kubernetes, Git, GitHub Actions, Unit Testing & Integration Testing
Databases & Storage
Azure Blob Storage, SQL Databases, NoSQL Databases, Vector DBs (FAISS, Chroma, Azure Vector Index)
Enterprise & Domain Knowledge
Automotive Safety Systems, Business Case Forecasting, Enterprise AI Integration, Data Governance, Security & Compliance
Data Engineering
Data pipeline development, PostgreSQL, Azure, ETL processes, data analysis, quality assurance, machine learning model integration
Mathematical Modeling
Monte Carlo Simulation, Option Valuation, Numerical Analysis, Heat Transfer, Flow Characteristics, Thermosyphon Heat Pipes, Computational Fluid Dynamics
Development Tools
Git, REST API Development & Integration, RPA (Microsoft Power Automate)
Generative AI, Large Language Models (GPT-4, GPT-4o), Agentic AI Systems, Retrieval-Augmented Generation (RAG), Predictive Modeling, Forecasting Models, NLP Pipelines, Embedding Models, Semantic Search, Vector Databases, Model Evaluation & Optimization, Prompt Engineering, Chain-of-Thought Orchestration, Model Context Protocol (MCP)
AI Platforms & Cloud
Azure AI Foundry, Azure Machine Learning, Azure Cognitive Search, Azure Functions, Azure App Service, Azure Kubernetes Service (AKS), LLMaaS (VQ), Cloud-Native AI Deployment, API Management
Software Engineering
Python, FastAPI, Flask, REST API Development, JavaScript/TypeScript, React, Web Development, Microservices Architecture, Docker, Kubernetes, Git, GitHub Actions, Unit Testing & Integration Testing
Databases & Storage
Azure Blob Storage, SQL Databases, NoSQL Databases, Vector DBs (FAISS, Chroma, Azure Vector Index)
Enterprise & Domain Knowledge
Automotive Safety Systems, Business Case Forecasting, Enterprise AI Integration, Data Governance, Security & Compliance
Data Engineering
Data pipeline development, PostgreSQL, Azure, ETL processes, data analysis, quality assurance, machine learning model integration
Mathematical Modeling
Monte Carlo Simulation, Option Valuation, Numerical Analysis, Heat Transfer, Flow Characteristics, Thermosyphon Heat Pipes, Computational Fluid Dynamics
Development Tools
Git, REST API Development & Integration, RPA (Microsoft Power Automate)
Sprachen
DeutschgutEnglischMuttersprache
Projekthistorie
Designed and implemented scalable, end-to-end AI agent workflows for business case financial forecasting, leveraging agentic Retrieval-Augmented Generation (RAG) pipelines to achieve high prediction accuracy. Utilized Azure AI Foundry and LLMaaS for model deployment, integrating advanced models such as GPT-4 and custom embeddings for enhanced performance. Developed and maintained robust data pipelines and backend infrastructure to support real-time data ingestion, processing, and model inference. Engineered and integrated Model Context Protocol (MCP) within the Business Case Agent, enabling seamless orchestration of multi-agent interactions and advanced API integrations. Built a secure, user-friendly web application for in-house deployment, facilitating cross-functional collaboration and efficient adoption of AI-driven business case analysis.
Developed scalable backend infrastructure and data pipelines using Python, PostgreSQL, Azure, and Databricks for automated data analysis, quality assurance, and machine learning model integration. Built robust ETL processes for complex simulation data (e.g., ANSYS optiSLang), including advanced feature engineering and validation routines. Created and trained machine learning models for anomaly detection and design deviation prediction (achieving > 98% accuracy), integrated into CI/CD validation workflows. Developed interactive visualization tools with FastAPI and Streamlit for real-time monitoring of model performance and status.
Conducted comprehensive analysis of electromechanical systems, focusing on electric motor design and performance improvement. Applied advanced statistical methods (Design of Experiments, R² analysis, confidence intervals, sensitivity analysis) to improve simulation accuracy and efficiency. Utilized Ansys Motor-CAD for detailed FEA-based electric motor simulations, ensuring precise thermal and electromagnetic assessments. Leveraged Ansys optiSLang for robust design optimization, enhancing engineering reliability through simulation-driven insights. Customized electric motor models to meet specific application requirements, delivering optimized solutions for diverse operational scenarios.