Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning

Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring

Jax, Tensorflow, Nlp, Pytorch, Python, ASR, Agentic RAG pipelines, ML lifecycle evaluation and deployment, Llm, TTS, state space models, self-learning agents, LLM orchestration, low-latency real-time ML applications

Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow

data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines

classification), NLP (tokenization, Oops, C, C++, Os Fundamentals, Objective-c, Tensorflow, Pytorch, Swift, Python, language modeling, modern agentic and multi-agent systems, decoding, RAG architectures

error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models

Machine Learning, Kafka, Sql, MLops, Docker, Spark, Python, Kubernetes, AWS, Ai, anomaly detection, Real-time data processing, Time-series forecasting