Senior AI Scientist
New
J
JobgetherArtificial Intelligence
Based in Germany, Core working hours from 10:00 AM to 3:00 PM local timeFull-TimeSenior
SalaryCompetitive salary with individual performance-based bonuses paid quarterly.
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningNLPPrompt Engineering
Requirements
- 5+ years of professional experience in AI, machine learning, NLP, or a related technical field.
- Strong practical understanding of LLMs, generative AI, and conversational AI systems.
- Hands-on experience with prompt engineering, prompt evaluation, and LLM optimization.
- Proven experience evaluating conversational AI using both qualitative and quantitative methodologies.
- Strong Python and SQL skills, with the ability to analyze data and build practical evaluation or experimentation workflows.
- Experience with AI evaluation frameworks, benchmarking, and systematic model or system assessment.
- Knowledge of RAG architectures, vector databases, embedding models, and retrieval optimization.
- Experience working with production AI systems at scale.
- Understanding of AI governance, safety, and responsible AI practices.
Responsibilities
- Analyze AI conversations, interaction data, and user feedback to identify opportunities to improve response quality, accuracy, relevance, and overall user experience.
- Design, implement, test, and optimize prompt engineering strategies and conversational behaviors.
- Optimize RAG pipelines, retrieval mechanisms, vector-based search, and overall retrieval quality.
- Evaluate different LLMs and recommend the most effective models for specific product use cases.
- Define, track, and continuously improve AI quality metrics such as accuracy, relevance, latency, and user satisfaction.
- Design and execute structured experiments to assess AI improvements and determine their impact.
- Develop and maintain evaluation frameworks, benchmarks, testing methodologies, and supporting documentation.
- Collaborate closely with AI Engineers to validate and deploy successful improvements into production environments.
- Monitor the performance of production AI systems and identify opportunities for optimization at scale.
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