Job Description
Requirements
Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc. ).
Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as Lo RA, prefix tuning, adapters, and quantization-aware training.
Proficiency in Py Torch, Tensor Flow, Hugging Face ecosystem, and good to have distributed training frameworks (e. g., Deep Speed, Py Torch Lightning, Ray).
Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments. ...
Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc. ).
Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as Lo RA, prefix tuning, adapters, and quantization-aware training.
Proficiency in Py Torch, Tensor Flow, Hugging Face ecosystem, and good to have distributed training frameworks (e. g., Deep Speed, Py Torch Lightning, Ray).
Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments. ...
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