What are the essential skills for an NLP Scientist in 2026?
- LLM Architecture: Deep understanding of Transformer variants, attention mechanisms, and scaling laws.
- RAG Engineering: Mastery of vector databases like Pinecone/Milvus and orchestration frameworks like LangChain.
- Optimization: Proficiency in quantization (QLoRA), model pruning, and inference acceleration using vLLM or TensorRT.
- Data Strategy: Expertise in RLHF, synthetic data generation, and curation of high-quality alignment datasets.
- Cloud Infrastructure: Ability to deploy and scale models using AWS SageMaker, Kubernetes, and distributed training frameworks.
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- Developed named entity recognition (NER) systems using spacy and crfsuite to extract insights from unstructured medical data.
- Built a custom preprocessing pipeline for noisy text data that increased downstream model accuracy by 15%.
- Managed the full ml lifecycle from data collection to production monitoring using MLflow.
- Led the development of a domain-specific Large Language Model using pytorch and huggingface transformers!, improving inference latency by 40%.
- Implemented Retrieval-Augmented Generation (RAG) pipelines to reduce hallucinations in customer-facing chatbots.
- Worked on fine tuning bert models for multi-label classification of legal documents, achieving a 0.92 f1-score.
- Collaborated with cross-functional teams to deploy scalable nlp services using Docker and Kubernetes.
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