Data Scientist

• Title : Senior Data Scientist
• Location : Raleigh, NC (Hybrid)
• Type: Contract

• Job Descriptio
• n:The Senior Data Scientist will coordinate complex cloud and artificial intelligence initiatives that leverage large language models and advanced machine learning within a hybrid work model. The role will orchestrate projects across AWS, GCP, or Azure environments, ensuring reliable Python based solutions that apply frameworks such as Keras, spaCy, and PyTorch to deliver measurable value for clients and communitie

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• Required Skills: Python, PyTorch, Machine Learning, Prompt Engineering, Large Language Models, AWS / GCP / Azure Cl

• oud
• Responsibilit
• ies:• Developed and implemented LLM-powered applications for legal domain use cases, enabling intelligent document analysis, search, summarization, and workflow automat
• ion.• Designed and maintained high-quality legal datasets, training corpora, and evaluation frameworks to ensure model accuracy, reliability, and compliance with business standa
• rds.• Built scalable data pipelines for ingestion, preprocessing, annotation, validation, and management of large-scale legal document repositor
• ies.• Collaborated closely with legal subject matter experts to gather requirements, translate business needs into AI solutions, and ensure domain-specific model effectiven
• ess.• Conducted model experimentation, benchmarking, and performance evaluations to improve NLP and Generative AI outcomes across legal workfl
• ows.• Evaluated AI/ML and GenAI outputs through both automated and human-in-the-loop validation processes to ensure quality, accuracy, and business alignm
• ent.• Designed and implemented NLP solutions using transformer-based architectures, embeddings, semantic search, document classification, clustering, and information extraction techniq
• ues.• Leveraged frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and LlamaIndex to develop, fine-tune, and deploy enterprise-grade AI applicati
• ons.• Developed prompt engineering strategies, retrieval-augmented generation (RAG) workflows, and model fine-tuning approaches to optimize LLM performa
• nce.• Worked with relational, NoSQL, and vector databases including PostgreSQL, Elasticsearch/OpenSearch, and ChromaDB to support AI-driven applicati
• ons.• Built and deployed scalable APIs and containerized AI services using modern cloud-native architectures across AWS, Azure, and GCP environme
• nts.• Participated in software design reviews, code reviews, MLOps/AIOps initiatives, and production deployment activities while following development best practices and governance standa
• rds.• Collaborated with cross-functional engineering teams to deliver secure, scalable, and production-ready machine learning solutions in global enterprise environme

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