Great Value Hiring

Key Responsibilities Build NLP pipelines for classification, tokenization, and retrieval-augmented generation (RAG). Design scalable text analytics workflows for multilingual corpora. Implement and fine-tune models (Hugging Face, PyTorch, fastText). Develop evaluation frameworks for model performance. Integrate NLP solutions into broader AI/search pipelines. Collaborate with researchers and engineers to deliver production-grade systems. Ideal Candidate Degree/background in computer science, computational linguistics, or related field. Proficient with Hugging Face Transformers, spaCy, tokenizers, PyTorch. Experienced with JSON/JSONL and building scalable data pipelines. Strong understanding of NLP tasks: classification, tokenization, entity recognition, retrieval. Skilled in preprocessing, embeddings, and model fine-tuning. Comfortable with multilingual datasets and evaluation benchmarks. Interested in RAG systems and neural search pipelines. Primary Goal Deliver robust NLP pipelines for classification, tokenization, and RAG at scale. Why Join Work on cutting-edge RAG and NLP pipelines. Use state-of-the-art NLP and IR tools. Contribute to global, multilingual AI systems. Operate at the intersection of AI, language, and scalable engineering.