Large Language Model (LLM) Expertise
- Strong understanding of modern LLM architectures (e.g., Transformer-based models such as GPT, BERT, LLaMA).
- Experience working with pretrained models and fine-tuning them for downstream tasks.
- Familiarity with inference optimization, model evaluation, prompt engineering, and deployment considerations.
- Knowledge of vector databases, embeddings, and retrieval-augmented generation (RAG) is a plus.
Training Data & Dataset Engineering
- Solid understanding of dataset curation, preprocessing, augmentation, and quality control.
- Experience working with large-scale text datasets, dataset formatting (JSONL, Parquet, etc.), and annotation workflows.
- Ability to design training/validation splits, understand data bias, and work with synthetic data generation pipelines.
Python & ML Tooling
- Proficiency in Python, with hands-on experience in NLP and ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, spaCy, or NLTK.
- Ability to write modular, efficient, and production-ready code for training, evaluation, and experimentation.
- Familiarity with ML ops, experiment tracking (Weights & Biases, MLflow), and data pipelines is advantageous.
NLP & ML Knowledge
- Understanding of classical NLP methods (tokenization, sequence labeling, text classification, embeddings).
- Knowledge of evaluation metrics for NLP tasks (BLEU, ROUGE, perplexity, accuracy, F1).
- Familiarity with fine-tuning techniques such as LoRA, PEFT, SFT, RLHF is desirable.
Problem-Solving & Analytical Abilities
- Strong analytical mindset with the ability to break down complex NLP challenges.
- Excellent debugging skills across model behavior, training pipelines, and data issues.
- Ability to design experiments, interpret results, and iterate quickly.
