Employer description & source details
Fine-tune transformer models and LLMs using techniques such as LoRA and QLoRA for entity extraction, classification, and reranking. Develop NLP models to extract, classify, and link entities from resumes, job descriptions, and other unstructured enterprise data. Build prediction models, knowledge graphs, and graph traversal solutions to represent relationships between skills, roles, and competencies. Design and implement semantic retrieval, clustering, and learning-to-rank pipelines across large taxonomies and datasets. Develop embedding-based search solutions using bi-encoders, cross-encoders, and related retrieval techniques. Apply classical machine learning methods, including gradient boosting, logistic regression, clustering, and dimensionality reduction. Design evaluation frameworks, select appropriate metrics, analyse model errors, and validate performance through systematic experiments. Explore datasets containing millions of records using Python, SQL, and data-processing tools. Deploy, monitor, and improve machine learning models in production environments. Use experiment tracking, model versioning, Docker, and cloud infrastructure to support reproducible ML workflows. Collaborate with product, data, and engineering teams to translate business problems into effective AI solutions. Document experiments, communicate findings, and continuously improve model accuracy, efficiency, and reliability. 1+ years of relevant experience in applied machine learning, NLP, or data science; experience expectations may vary between Data Scientist I and II. Strong proficiency in Python , with hands-on experience using PyTorch, Hugging Face Transformers, sentence-transformers, pandas, and scikit-learn. Practical experience fine-tuning transformer models on custom datasets for named
Extracted skills: python, sql, aws, docker, machine learning