Square One Resources
Senior Data Scientist
Senior Data Scientist developing production-ready AI and LLM solutions for a client project. The role focuses on RAG architectures, AI agents, and orchestration frameworks like LangChain or LlamaIndex using production-grade Python.
- Compensation
- Not disclosed
- Location
- Warsaw, Poland
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data science
- Posted
Skills
Must have
- 6+ years of experience in Data Science/AI engineering
- 4+ years of experience in production-ready Python AI-related code development
- 2+ years of experience in production-ready LLM-related code development
- Strong knowledge in Generative AI, chatbots, and AI agents
- Understanding of LLM evaluators, validators, and guardrails
- Hands-on experience with LangChain, LlamaIndex, or LangGraph
- Experience designing or operating MCP servers/clients for LLM agents
- Strong Python skills (production-grade code, packaging, testing)
- Solid understanding of ML/AI concepts and model lifecycle
Nice to have
- Experience with Retrieval-Augmented Generation (RAG)
Full description
We're looking for Senior Data Scientist to join our client's project
- 6+ years of experience in Data Science/AI engineering.
- At least 4+ years of experience in production-ready Python AI-related code development.
- At least 2+ years of experience in production-ready LLM-related code development, preferably based on the Retrieval-Augmented Generation (RAG) concept.
- Strong analytical and problem-solving skills with the ability to optimize AI solutions for diverse applications.
- Strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
- Deep understanding of LLM evaluators, validators, and guardrails.
- Hands-on experience with one or more GenAI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration stacks.
- Hands-on experience designing or operating MCP servers/clients for LLM agents
- Strong Python skills, including production-grade code, packaging, and testing for data/ML services
- Solid understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, AI architectures.