Software Developer
FDTech GmbH
Total years of experience :6 years, 4 Months
• Acted as product owner, engaging directly with stakeholder at CARIAD Volkswagen Group to gather and discuss requirements for developing and maintaining automated CI/CD pipelines for code deployment for the projects - Adaptive Cruise Control and Highway pilot systems. The result was reduced deployment time, facilitating faster iterations and updates to critical automotive systems. Leveraged Python, Groovy, Jenkins, and GitHub Actions for pipeline development and management.
• Built from the ground up a SaaS tool for Configuration Management to meet government compliance requirements, effectively managing product components including software releases, version control, and documentation (Bitbucket/Confluence Atlassian). Leveraged frontend technologies such as HTML and CSS for user interface design, while employing Python, Javascript, Flask framework, and MongoDB for backend development. Orchestrated containerization with Docker and optimized performance using NGINX web server. Collaborated with cross-functional team to receive feedback and code review.
• Automated nightly processes using Azure Databricks, ensuring smooth handling of sensor data from a simulated car using virtual carmaker software. Designed data processing in Databricks notebook for data reading, data cleaning, and data processing from the Data Lake. Time- efficient scheduling and resource optimization were implemented by strategically selecting memory and core configurations in cluster to optimize cost-effectiveness and performance. Primarily tools used: Python, Databricks notebook, Spark, MySQL framework.
• The processed data is seamlessly exported to a PostgreSQL database, serving as the foundation for comprehensive analysis and reporting through Power BI. Facilitated efficient data management and analytics, empowering data-driven solution and decision-making and insights generation for stakeholders and decision-makers.
• Designed and Implemented A/B testing to optimize software features using key performance indicators KPIs or metrics such as Collision Detection accuracy and Lane Keeping Performance. This improved the effectiveness and reliability of automotive systems, enhancing overall safety and performance.
• Reinforced best practices by creating thorough unit, integration, and functional tests in a containerized and more robust way, reducing post- deployment issues and enhancing product stability and immediately troubleshotting issues as per user requirements and for customer satisfaction. Primarily tools used Python, PyUnit and Docker.
• Implemented an iterative approach to develop and deploy a machine learning model that predicts energy consumption using wind/solar predictions for management and optimizing the consumption of energy.
• Deployed the model on Microsoft Azure Machine Learning Batch Pipeline Model, enabling easy management and scalability. Leveraged Microsoft Azure Cloud Services like Azure ML Flow and created a full-stack application by connecting a storage account to a blob-triggered function app and SQL DB/Server, enabling seamless data processing and storage.
• Post-processed the model by comparing the energy consumption forecast with energy generation from wind and solar predictions thereby creating a relationship between energy consumption and renewable energy generation.
• Leveraged real-time visualization and analysis of energy consumption patterns to establish a clear relationship between energy usage and renewable energy generation. This supported data-driven decision-making and active energy management strategies by utilizing insights derived from forecasted data within the Power BI tool.
• Led a team of four for a Google Analytics project, which involved market and customer analysis of the Google merchandise store. Visualized industry trends and made informed decisions.
• Utilized machine learning models for forecasting customer churn, shopping behavior, and sales revenue over some time and performed data manipulation, visualization, and statistical analysis, to analyze the data and derive insights. Primarily tools used Python, Jupyter notebook, Google Data Studio, Pandas, scikit-learn, dash, bokeh, TensorFlow, Keras, Heroku cloud, ML models like Decision tree, k-means clustering and time series models.
• Conducted detailed optical characterization of laser diodes and managed experiments with streak cameras to collect and analyze spectral and temporal data.
• Designed and implemented optical setups using fibers and lenses to automate data acquisition with streak cameras, optimizing measurement efficiency.
• Applied advanced statistical analysis and mathematical modeling to interpret laser diode dynamics and optimize performance.
• Instructed practical sessions on IoT, utilizing esp32 microcontrollers to develop Bluetooth-enabled health monitoring systems, analyzing real-time data streams.
• Employed Fourier analysis and Digital Signal Processing techniques, including FFT, to perform data transformations and extract meaningful insights from complex datasets.
Masters in Nanotechnology Materials Science Surface thin films
Real-time Embedded Systems Computer programming Digital Systems Electronics systems Electrical Design Communications and Signal processing
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