PyTorch

Builds basic CNNs in PyTorch for image classification using torchvision pretrained models (ResNet, VGG). Writes custom Dataset and DataLoader for image data. Applies torchvision.transforms for augmentation: random crop, flip, color jitter. Understands training loop basics.

Builds simple neural networks in PyTorch for tabular data. Uses nn.Module for model definition. Understands tensor operations, autograd and basic training loop. Experiments with loss functions and optimizers (Adam, SGD). Tracks metrics across experiments.

Understands PyTorch tensor operations and autograd for transformer models. Uses HuggingFace Transformers with PyTorch backend for inference. Loads pretrained LLMs, runs tokenization and text generation. Grasps attention mechanism basics in PyTorch.

Implements detection (YOLO, SSD) and segmentation (U-Net, Mask R-CNN) in PyTorch. Fine-tunes pretrained models with custom heads and layer freezing. Builds augmentation pipelines with Albumentations. Processes video with efficient batching and temporal models.

Designs custom architectures with nn.Module for complex experiments. Uses PyTorch Lightning for structured training with callbacks and early stopping. Tunes hyperparameters with Optuna integration. Implements custom loss functions and LR schedulers. Profiles training bottlenecks.

Fine-tunes transformers with PyTorch using PEFT: LoRA, QLoRA, prefix tuning. Implements custom training loops for causal LM and seq2seq tasks. Works with tokenizers, attention masks and padding strategies. Integrates HuggingFace Trainer with DeepSpeed for efficient fine-tuning.

Designs end-to-end CV systems in PyTorch: multi-task learning, knowledge distillation, custom losses for detection/segmentation. Optimizes inference with TorchScript, quantization and pruning. Builds real-time video pipelines with efficient GPU memory management.

Architects complex PyTorch training with distributed data parallelism. Implements custom autograd functions for research. Leads architecture decisions: attention mechanisms, residual connections, normalization. Mentors team on PyTorch debugging and profiling with torch.profiler.

Architects LLM training infrastructure in PyTorch. Implements custom attention, positional encodings and model parallelism. Optimizes memory with gradient checkpointing, mixed precision and Flash Attention. Designs evaluation frameworks for LM quality. Mentors team on transformer internals.

Defines PyTorch strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Defines PyTorch standards for DS team: experiment structure, reproducibility requirements, model versioning. Evaluates ecosystem tools (Lightning, TorchRec) for team adoption. Reviews architectural decisions in training pipelines. Drives knowledge sharing on advanced patterns.

Defines PyTorch-based LLM training standards: PEFT strategy selection, distributed training configs, evaluation benchmarks. Evaluates tools (vLLM, TensorRT-LLM) for production inference. Reviews fine-tuning architectures. Establishes best practices for reproducible LLM experiments.

Defines PyTorch strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.

Shapes org-wide PyTorch strategy: version governance, TensorFlow migration, GPU cluster policies. Drives adoption of torch.compile and torch.export across teams. Defines cross-team standards for training infrastructure and model deployment. Influences ecosystem through contributions.

Shapes org-wide LLM training platform on PyTorch: multi-team GPU allocation, model parallelism standards, pre-training vs fine-tuning investment. Drives PyTorch 2.x compiler adoption for LLM workloads. Defines cross-team standards for model artifacts and cost optimization.