Data Scientist – GenAI / LLM / RAG

Company Overview

(No specific information provided about the company.)

Job Summary

We are seeking a Data Scientist with a strong focus on Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) architecture. This is a high-priority role aimed at quickly closing the position with qualified candidates who possess hands-on experience in the required areas.

Responsibilities

• Develop and implement RAG pipelines using tools such as LangChain, LangGraph, and FAISS or Pinecone.
• Utilize Large Language Models including GPT, Claude, and LLaMA with a focus on prompt engineering.
• Write efficient and scalable code in Python, employing frameworks including FastAPI, Hugging Face, TensorFlow, or PyTorch.
• Manage cloud deployment across platforms such as Azure, Google Cloud Platform (GCP), or Amazon Web Services (AWS).
• Work with vector databases, embedding models, and partake in LLMOps processes to streamline AI operations.
• Ensure real-world implementation experience in Generative AI rather than theoretical exposure.

Qualifications

• Education: Bachelor’s or Master's degree in Computer Science, Data Science, Mathematics, or a related field.
• Experience: 4–8 years in data science or related fields with a strong emphasis on AI and machine learning.
• Skills:
  • Proficient in Python programming.
  • Experienced in Large Language Models and prompt engineering techniques.
  • Hands-on experience with RAG pipeline building.
  • Familiarity with cloud platforms (AWS, Azure, GCP).
  • Knowledgeable in vector databases and embedding models.

Preferred Skills

• Familiarity with advanced AI frameworks and tools.
• Exposure to the latest developments in Generative AI and related technologies.

Experience

• Minimum of 4 years and a maximum of 8 years in the relevant field.
• Real-world implementation experience is mandatory; theoretical knowledge is not sufficient.

Environment

(No specific information provided about the work setting, location, or conditions.)

Salary

(No specific salary range provided.)

Growth Opportunities

(No specific information provided about career advancement opportunities.)

Benefits

(No specific information provided about benefits.)