Proven ML/DL/NLP background; fine-tune large-scale models (LLMs, transformers).
PEFT expertise: LoRA, prefix tuning, adapters, and QAT.
Proficient in PyTorch, TensorFlow, Hugging Face; DeepSpeed/Lightning/Ray exposure.
MLOps basics: experiment tracking, model versioning, CI/CD, prod deployment.
Experience with large datasets and data pipelines; Spark/Databricks or cloud ML services.
GPU/TPU optimization and mixed-precision training for scaling.
3+ years in applied ML; Master's/PhD preferred.

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