Senior Data Scientist – GenAI / RAG

Prudent Technologies and Consulting, Inc.; Dallas (TX); Vor Ort; USD 140.000 - 200.000; Vollzeit

Zusammenfassung

Prudent Technologies and Consulting, Inc. seeks a Senior Data Scientist to design and deploy GenAI and Retrieval-Augmented Generation (RAG) solutions for enterprise clients in Dallas.

You will translate complex business problems into scalable ML and AI strategies and collaborate with product and engineering teams. The ideal candidate has hands-on experience with LangGraph, LangChain, MCP, vector databases, AWS, and MLOps, plus strong Python and SQL skills and a solid background in ML,

Qualifikationen

• Hands-on experience with Agentic AI and multi-agent systems.

Aufgaben

• 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.

Kenntnisse

Python

R

TensorFlow

PyTorch

Scikit-learn

SQL

Tableau

Power BI

Spark

Hadoop

LangChain

LangGraph

MCP

Vector Databases

AWS

MLOps

Tools

LangGraph

LangChain

MCP

Vector Databases

Jobbeschreibung

Job Title : Senior Data Scientist – GenAI / RAG

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

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.