Senior Data Scientist – GenAI / RAG

Job Title : Senior Data Scientist – GenAI / RAG Location : Houston, TX ,Dallas TX, or Santa Clara CA ( Hybrid ) Job Type : Full-time Job Description Strong hands-on experience in Agentic AI and Multi-Agent Systems Experience with LangGraph, LangChain, MCP (Model Context Protocol), tool calling, agent orchestration Strong RAG implementation experience including Vector Databases, Hybrid Retrieval, Reranking, Knowledge Graphs Hands-on experience with AWS Bedrock and enterprise GenAI solutions Strong Python and SQL skills Solid Traditional Data Science / Machine Learning background: Classification Regression Forecasting Anomaly Detection Feature Engineering Model Evaluation Experience with XGBoost, CatBoost, Random Forest, Deep Learning frameworks (PyTorch/TensorFlow) Experience designing and deploying production AI/ML systems Understanding of MLOps / LLMOps, model monitoring, evaluation, observability, and retraining pipelines Ability to translate business problems into ML or Agentic AI solutions Experience with LLM Evaluation, Hallucination Detection, Groundedness and Retrieval Quality metrics Exposure to Databricks, Spark, Vector Databases, APIs, Cloud Platforms (AWS preferred) Required Skills : Strong proficiency in programming languages such as Python or R.

Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Solid understanding of statistical analysis techniques and data modeling. Proficiency in data visualization tools (e.g., Tableau, Power BI).

Familiarity with big data technologies (e.g., Hadoop, Spark). Ability to work with databases and query large datasets using SQL. Strong problem-solving skills and the ability to think critically.

Excellent communication and collaboration skills.

Key Responsibilities

• Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces Work closely with client data science teams (ML/DL ecosystems) Develop GenAI-based enterprise knowledge solutions Collaborate directly with stakeholders and customers Develop and implement machine learning algorithms to solve complex business problems.
• Analyze large datasets to generate insights and inform decision-making processes.
• Collaborate with product managers and engineers to integrate data science solutions into enterprise products.
• Communicate findings and recommendations effectively to technical and non-technical stakeholders.
• Stay current with the latest advancements in data science and machine learning technologies.
• Tech Environment AWS ecosystem Snowflake (data platform)