NLP Engineer

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.