Data Scientist

We’re looking for an experienced Data Scientist with a strong background in Prompt Engineering and Generative AI to join our cutting-edge AI research team for a limited-term project. In this role, you’ll be instrumental in advancing our Retrieval-Augmented Generation (RAG) capabilities, fine-tuning language model prompts, and implementing intelligent systems that generate insights from diverse data sources. This is a perfect opportunity for someone who enjoys experimentation and is passionate about expanding the frontiers of large language model applications.

Core Responsibilities

  • Design and refine prompt strategies to improve the quality, precision, and value of AI-generated content.
  • Develop and manage RAG workflows leveraging Atlas Vector Search, SQL-based databases, and proprietary data.
  • Build and evaluate ML models with a focus on retrieval-based frameworks and agent-like architectures.
  • Plan and execute A/B testing and empirical studies to assess and optimize prompt performance.
  • Monitor user engagement and system metrics to guide iterative improvements.
  • Work closely with teams across product, engineering, and content to ensure AI solutions align with strategic goals.
  • Develop reproducible machine learning pipelines using Docker, supporting production-level deployment.
  • Build intuitive dashboards and reporting tools to showcase the performance and business impact of AI solutions.

Required Skills & Experience

  • Demonstrated experience crafting and tuning prompts for large language models (LLMs), especially those in the GPT-4 era.
  • Practical knowledge of RAG architecture and semantic search with Atlas Vector Search.
  • Strong Python skills for scripting, data manipulation, and model development.
  • Solid understanding of relational databases such as PostgreSQL or MySQL for data querying and integration.
  • Proficiency with Docker for creating portable and consistent ML environments.
  • Familiarity with designing data-driven experiments and interpreting results effectively.
  • Excellent written and verbal communication skills, with the ability to explain complex AI concepts in a clear, accessible way.

Bonus Skills

  • Experience with Agentic AI systems or reasoning-based architectures.
  • Exposure to Apache Kafka for real-time data streaming and pipeline development.
  • Knowledge of Kubernetes (K8s) for scaling and orchestrating ML workloads.
  • Backend development familiarity, especially using Node.js.
  • Understanding of user experience (UX) principles and tools such as Streamlit, Dash, Power BI, or Tableau.

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