Engineering Manager

• Proficiency in Python with TensorFlow/Keras: model building, fit(), callbacks, and checkpointing
• Understanding of CNN architectures, especially ResNet (residual connections, skip layers, BatchNorm) but not just limited to these.
• Transfer learning: using pretrained ImageNet weights and fine-tuning for custom datasets
• GPU-aware training: mixed precision (tf.float16), tf.data pipelines, multi-GPU strategy on DGX Spark
• Data handling: image preprocessing, augmentation (tf.image), tfrecords, tensorflow profiling.
• Solid C (not just general C - but embedded C specifically): bitwise operations, uint8_t / uint32_t, structs, control register manipulation
• Digital logic fundamentals - they need to think in hardware terms: clock cycles, control signals, overflow flags and two's complement
• Simulation mindset: writing software that models hardware behaviour before it runs on silicon

Education:- Diploma/Bachelor/Masters Engineering