Senior Data Scientist (GenAI)
New
T
TQLO SP. Z O.O.Generative AI
Workplace type: remote; Tryb Współpracy: 100% zdalnieContractSenior
Salary165 - 180 PLN per hour net b2b
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Job Details
- Languages
- Pl B2, en C1; very good knowledge of English enabling communication in an international environment
- Experience
- Minimum 6 years of experience in Data Science or AI; minimum 2 years of experience with production solutions using LLMs
- Required Skills
- PythonLangChain
Requirements
- At least 6 years of experience in Data Science or AI.
- At least 2 years of experience with production solutions using LLMs.
- Strong Python skills and the ability to write high-quality, testable production code.
- Practical experience designing and developing RAG systems.
- Strong knowledge of LLMs, agent systems, and conversational solutions.
- Experience with vector search, hybrid search, reranking, and context retrieval optimization.
- Practical knowledge of LangChain, LangGraph, or a similar GenAI framework.
- Experience with MCP and integrating models and agents with external tools or data sources.
- Strong understanding of LLM evaluation and experience measuring generative solution quality.
- Strong machine learning fundamentals, including evaluation methods, metrics, and model lifecycle.
- Experience with cloud solutions such as Azure, AWS, or GCP; knowledge of CI/CD, testing, and containerization.
- Ability to translate business problems into technical concepts and solutions; strong analytical and complex problem-solving skills.
- Very good English for communication in an international environment.
Responsibilities
- Design GenAI solutions for business and technical needs, selecting models and approaches for each use case.
- Develop production solutions using LLMs, RAG, and agent systems.
- Build mechanisms for using knowledge from multiple data sources.
- Design and optimize search, processing, and context delivery for models.
- Create methods to measure the quality, stability, and effectiveness of AI solutions.
- Experiment with models and adaptation methods for specific applications.
- Improve control over model responses and the security of AI solutions.
- Optimize solutions for quality, performance, scalability, and cost.
- Design integrations between AI components, existing systems, and data sources.
- Assess new models, frameworks, and approaches for production use, and contribute reusable components and technical standards.
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