ML Architect

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 28,200+ 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.

Roles & Responsibilities : expertise in Python, which is the programming language of choice for Machine Learning expertise in a common ML framework, preferably PyTorch, which we've been using (or TensorFlow, both are huge+popular)

knowledge of deep neural networks (DNNs), in particular recurrent neural networks (RNNs) such as LSTMs, which we've been using includes knowledge about optimizers, loss functions etc.

maybe also expertise in time series prediction this is specific to the conferencing system

time series prediction and recurrent neural networks are closely linked for other products, it might be less relevant other big types of DNNs (besides RNNs) are:

Convolutional Neural Networks (CNNs), especially for image recognition Generative Adversarial Networks (GANs), especially for synthetic data generation

Transformers (more recent hype), especially in LLMs or other time-series prediction problems where the time series span over very long horizons (like text, which can have correlation over whole chapters)

there’s more, but these are the really big ones I’d say, so the candidate should know what they’re used for

experience in implementing neural network architectures in a machine learning framework

i.e. have they worked on implementation projects before

experience in training and evaluating ML models with large data sets, preferably using TensorBoard

TensorBoard is the software that collects and visualizes model performance data during/after training

knowledge in statistics, in particular probability distributions, and experience in evaluating model performance and system benchmarks in statistically sound ways, such as using empirical distribution functions (i.e. cumulative distribution functions (CDFs) and similar), 95%-confidence intervals, etc.

Educational qualification:

B.E/B.Tech in ECE/CS/IT

Experience :

10 years

Mandatory/requires Skills :
ML/Ops, Python, Pytorch, Docker, Kubernates, DevOps, Tensorboard, GNNs, CNNs, Transformers, Tensor Flow

Preferred Skills :