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Data Scientist GenAI / RAG

Prudent Technologies and ConsultingSanta Clara, CA🇺🇸United StatesPosted Oct 14, 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Santa Clara, CA, United States
Posted
Yesterday
SQLAWSMLOpsMachine LearningScikit-learnSnowflakeTableauDatabricksDeep LearningHadoopLLMPower BIPyTorchPythonTensorFlow

Job Description

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)

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

Tech Environment

  • AWS ecosystem
  • Snowflake (data platform)
    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.

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