André (Dr.) Gensler
MLOps, Python, Azure Cloud Engineer with Energy Economics Background
Experience
Schaumann GmbH
- Full conception, design, implementation, and operation of a voice chatbot
- Evaluation of different implementation concepts (STT, TTS, Speech-to-Speech)
- Integration into telephony platforms
- Implementation of dashboards and KPIs
- Defining and aligning customer requirements
- Technologies used: FastAPI, Deepgram, Elevenlabs, langchain, langgraph, GitHub Actions, Streamlit, Vonage
Munich RE AG
- Developed concepts and executed migrations for GenAI use cases
- Defined and migrated role-based access control (RBAC) concepts across different cloud environments
- Migrated cloud components between AWS and Azure, including VNET integration
- Technologies used: Azure Entra, Azure DevOps, VNET, Kubernetes, Databricks, and more
E.ON Energy Markets GmbH
- Designed and developed an MLOps platform based on AzureML
- Integrated algorithmic energy trading models with exchange trading systems
- Created ETL jobs to run machine learning models
- Set up DevOps processes with Azure DevOps
- Technologies used: Azure ML, Azure DevOps, Python, Kubernetes, and more
Vaillant Group
- Developed various data pipelines to transform Apache Kafka data into analytics tables (Delta Tables, MS SQL Server) using PySpark
- Designed and created NoSQL database schemas for big data processing (Data Vault / Data Mart / 3rd Normal Form)
- Automated ETL job orchestration with Databricks
- Managed data infrastructure in Azure, including GitHub Actions, Azure DevOps, and Azure Storage Accounts
- Technologies used: Databricks, PySpark, GitHub Actions, Azure Storage Accounts
Munich RE AG
- Designed and developed a compliant MLOps solution for the machine learning model lifecycle in reinsurance
- Built solutions for model monitoring
- Developed a CI/CD system using Azure DevOps pipelines
- Implemented centralized logging for multiple Azure services via Azure Monitor
- Technologies used: Databricks, Azure DevOps, Kubernetes, Python, and more
SMA Solar Technology AG
- Automated global economic optimization of production planning using linear programming
- Visualization and presentation of the results to internal stakeholders
- Technologies used: Python, python-mip, pandas, JupyterLab, Plotly
SMA Solar Technology AG
- Conceptualized and provided technical consulting for implementing the algorithmic trading component of the overall project
- Analyzed and quantitatively evaluated backtesting datasets from market price forecast providers
- Technologies used: Python, python-mip, pandas, Azure DevOps, Docker
SMA Solar Technology AG
- Independently designed and delivered a multi-day specialized training on data analysis with Python multiple times
- Technologies used: Python, NumPy, pandas, Matplotlib, Jupyter, scikit-learn
SMA Solar Technology AG
- Data-driven forecasting of sales figures for individual product groups based on historical sales data and market development models
- Visualization and presentation of the results to internal stakeholders
- Technologies used: Python, scikit-learn, Prophet, statsmodels, pandas, Plotly
SMA Solar Technology AG
- Introduced a CI/CD process for an operational Python analysis framework with Gitflow-like actions (automated unit/integration testing, semantic versioning, automated containerization, automatic deployment)
- Implemented using Azure DevOps in a hybrid cloud/on-premises infrastructure
- Technologies used: Azure DevOps, Azure Container Registry, Azure Artifacts, Python, Shell scripts, Ansible, SemVer, Docker
SMA Solar Technology AG
- Creation of a configurable Python framework for conducting predictive maintenance analyses and building internal data processing libraries and pipelines
- Development of expert functions to detect specific faults in photovoltaic inverters
- Prediction of failure probabilities for individual components using isotonic regression models
- Technologies used: Python, pandas, NumPy, scikit-learn, SQLAlchemy, PyArrow
University of Kassel
- Carried out complete machine learning experiment workflows (preprocessing, data filtering, feature engineering, experiment design) for power forecasting of wind turbines (intraday and day-ahead horizons)
- Created parametric and nonparametric density forecasts of power output
- Developed a new ensemble technique to dynamically weight and combine individual density forecasts into a mixed ensemble model
- Gained foundational knowledge in grid simulation for BMWi funding applications
- Technologies used: MATLAB, Neural Net Toolbox, Machine Learning Toolbox, Optimization Toolbox, Global Optimization Toolbox
University of Kassel
- Developed an algorithm to detect events in time series using supervised learning principles
- Evaluated the online-capable algorithm with synthetic and real data (e.g., ECG data streams)
- Published the algorithm on Springer Link
Skills
Software Engineering (Python). Solid Knowledge In Designing Software Frameworks With Implemented Applications In Platform Engineering, Predictive Maintenance, And Computer Vision.
Cloud Engineering & Devops. Several Years Of Operational Experience Running Multiple Python Software Services In Native And Hybrid Cloud Environments Using Azure Devops (Continuous Integration / Deployment, Containerization, Monitoring, Etc.) And Many Azure Services.
Machine Learning, Pattern Recognition, Time Series Forecasting With Implemented Applications In Predicting Renewable Energy Output, Energy Market Trading, And More.
Energy Forecasting And Trading. Phd In Energy Forecasting, Several Years Of Energy Economics Background With Applications Including Energy Trading.
Python. 9 Years. Daily Use For Data Engineering And Machine Learning.
Matlab. 7 Years. Long-term Experience With Ml & Statistical Toolboxes.
Sql. 4 Years. Performing Inter-table Queries.
Pandas. 8 Years. Preparing And Cleaning Heterogeneous Time Series Data.
Mlflow. 6 Years. Ml Model Life-cycle Management.
Pytest. 6 Years. Unit/integration Testing For Software Frameworks.
Azure Sdk. 4 Years. Extensive Use Of Many Azure Sdk Features.
Restful Api. 5 Years. Used As Front-end For Microservices With Flask, Fastapi & Openapi.
Pydantic. 3 Years. Robust Rest Api Design.
Numpy. 6 Years. Accelerating Loops With Vector Operations.
Scikit-learn. 6 Years. Implementing Ml Models For Microservices.
Sqlalchemy. 4 Years. Introducing An Orm System Into A Software Framework.
Plotly. 3 Years. Creating Interactive Plots And Dashboards.
Statsmodels. 2 Years. Time Series Analysis And Forecasting.
Git. 7 Years. Designing Various Branching Workflows In Ci/cd.
Docker. 3 Years. Developing Containers For Microservice Deployment.
Scripting. 5 Years. Shell Scripting On Windows And Linux (Bash, Powershell).
Azure Devops. 5 Years. Operating A Software Framework (Ci/cd).
Azureml. 4 Years. Development & Automated Endpoint Deployment In Azureml.
Databricks. 3 Years. Using Various Databricks Apis.
Managed Identity (Mi). 5 Years. Using Managed Identity Authentication.
Cosmosdb. 3 Years. Designing Schema-less Nosql Databases.
Aci. 2 Years. Configuration & Deployment With Azure Container Instances.
Azure Functions. 2 Years. Creating Serverless Functions.
Vnet. 2 Years. Designing Vnet Architectures Including Private & Service Endpoints.
Acr. 4 Years. Container Management With Azure Container Registry.
Keyvault. 5 Years. Using Credentials Without Explicit Code Storage.
Aad Auth. 2 Years. Developing Authentication Workflows With Microsoft Entra.
Languages
Education
University of Kassel
Doctor of Natural Sciences, Wind Power Ensemble Forecasting – Performance Measures and Ensemble Architectures For · Energy Forecasting · Kassel, Germany · Grade 1.0
Aschaffenburg University of Applied Sciences
Master of Engineering · Electrical and Information Engineering · Aschaffenburg, Germany · Grade 1.1
Aschaffenburg University of Applied Sciences
Bachelor of Engineering · Electrical and Information Engineering · Aschaffenburg, Germany · Grade 2.0
Certifications & licenses
Cambridge BEC Certificate
Cambridge
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