Sr Data Scientist GenAI

Sr Data Scientist (NLP / LLM / Generative AI) Location: Dallas, TX Roles & Responsibilities :

• Design, build, fine-tune, and deploy LLMs, transformer-based NLP models, and GenAI solutions for both batch and real-time/streaming contexts.
• Own all major components of ML pipelines: data ingestion, cleaning, pre-processing (structured & unstructured), embedding, search & retrieval, prompt engineering, RAG (Retrieval-Augmented Generation).
• Collaborate closely with ML Engineers, MLOps, software engineering, product, compliance, legal etc., to move models from prototype to production-ensuring reliability, scalability, monitoring, and maintainability.
• Define and implement evaluation frameworks: accuracy, bias, fairness, hallucination, consistency, latency; run UAT, stress-tests, drift detection.
• Optimize models and pipelines for performance, cost, and efficiency.
• Ensure best practices in model development: version control, repeatability, documentation, governance, and ethical AI use.
• Mentor more junior data scientists; help build team skills in NLP, GenAI practices, prompt engineering, fine-tuning.
• Identify new use cases; prototype innovations in GenAI/NLP; keep up with latest research and open source developments, decide what to adopt.

• 10+ years of experience in data science / ML, with substantial work in NLP, LLMs, or Generative AI.
• Deep hands-on experience in Python, using frameworks like PyTorch, TensorFlow, HuggingFace etc.
• Proven track record building transformer/NLP / LLM models; experience with fine-tuning, prompt engineering.
• Solid experience with information retrieval / search: keyword + semantic search, embeddings, vector databases.
• Experience working in production / deploying models (batch and streaming), working with MLOps practices.
• Strong algorithmic / statistical / mathematical fundamentals. Ability to reason about model behaviour, bias, uncertainty.

• Good communicator: able to translate complex technical detail to business / non-technical stakeholders. Nice to Have:

• Master’s in Computer Science, Computational Linguistics, Statistics, Machine Learning or related field.
• Experience with multimodal models (vision + text) or emerging LLMs and agent-based systems.
• Experience with open source LLMs & toolkits; familiarity with LangChain or similar frameworks.
• Prior experience in regulated environments (finance, risk, legal, compliance) with strong governance, privacy requirements.