Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.
We are seeking a talented Applied AI/ML Engineer to develop sophisticated models that leverage both visual and time-series data.
In this role, you will focus on designing and implementing advanced algorithms to extract meaningful patterns, make accurate predictions, and solve complex analytical challenges.
- Your primary responsibility will be hands-on model development (for deploying in to automotive domain), from data analysis and feature engineering to training and evaluation.
- Key Responsibilities:Advanced Model Development Design, build, and validate machine learning models for a variety of complex tasks, including: Computer Vision: Object Detection, Image Segmentation, Facial Analysis, and Classification.
- Time Series Analysis: Forecasting, Anomaly Detection, and Classification of sequential data.
- Multi-Modal Modeling: Fusing visual and temporal data to create more powerful predictive models.
- Algorithm Implementation Implement and train a wide range of models, including Deep Learning architectures (CNNs, Transformers, LSTMs, GRUs) and traditional statistical methods (e.g., ARIMA, Prophet).
- Stay current with state-of-the-art techniques and apply them to our unique datasets.
- Data Analysis & Feature Engineering Conduct in-depth exploratory data analysis to understand data characteristics and identify predictive signals.
- Develop and implement novel feature engineering techniques tailored to both image and time-series data.
- Model Optimization & Evaluation Focus on building computationally efficient models.
- Rigorously evaluate model performance using appropriate metrics and validation strategies to ensure robustness and accuracy.
- Optimize model architecture and hyperparameters to achieve desired performance targets.