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....