Data Scientist
Core
Develop and deploy production-grade machine learning, NLP, and LLM solutions for entity extraction, classification, semantic retrieval, and knowledge graph applications using large-scale enterprise datasets.
Role type
Applied machine learning engineer (NLP/LLM)
Builds
Intelligent systems for entity extraction, classification, semantic retrieval, ranking, and knowledge graphs
Domain
Enterprise AI, Natural Language Processing, Large Language Models
Deliverable
production ML models
Required skills
Python, transformer models, applied NLP, machine learning, model evaluation, production deployment, SQL, data processing
Preferred skills
LoRA, QLoRA, PEFT, embeddings, attention mechanisms, contrastive learning, semantic search, learning-to-rank, knowledge graphs, Neo4j, Cypher, pgvector, AWS Bedrock, SageMaker
Technologies
PyTorch, Hugging Face Transformers, sentence-transformers, pandas, scikit-learn, Docker, AWS, vLLM, ONNX, GLiNER, NuNER
Responsibilities
Fine-tune transformer models and LLMs for entity extraction and classification; Develop NLP models to extract and link entities from unstructured data; Build prediction models and knowledge graphs; Design semantic retrieval and learning-to-rank pipelines; Develop embedding-based search solutions; Apply classical machine learning methods; Design evaluation frameworks and validate model performance; Deploy and monitor ML models in production; Use experiment tracking and model versioning; Collaborate with product and engineering teams to translate business problems into AI solutions.
Seniority
Mid-level, hands-on IC