Netomi - Data Scientist
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Requirements
• Research & Learning - Stay current with foundational LLM concepts, architectures, and developments in the field. Learn about transformer architectures, prompt engineering, and basic model optimization, Explore new LLM capabilities and frameworks under guidance from senior team members. Read and understand research papers and technical documentation • Solution Design & Prototyping - Support the design of AI solutions for business problems using LLMs. Build proof-of-concept applications to test ideas and validate approaches. Prototype LLM-based applications using modern frameworks. Conduct experiments to compare different approaches. Collaborate with team members and learn from subject matter experts • Development & Implementation - Write clean, readable code following team standards and best practices. Develop LLM-based workflows using orchestration frameworks (such as LangChain, LangGraph, or similar). Implement LLM features, including basic prompt engineering, API calls, and structured outputs. Build functional systems that integrate with LLM APIs. Participate in code reviews and learn from feedback. Optimize code for readability and basic performance considerations • Experimentation & Analysis - Design simple experiments to test ideas and measure results. Evaluate LLM outputs and system performance using established metrics. Work on prompt optimization and basic fine-tuning approaches. Conduct data analysis and statistical testing • 1-2 years of experience in data science, machine learning, software development, or related technical roles • Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or related technical field • Demonstrated interest in NLP and LLM applications through projects, coursework, or work experience • Strong Python programming skills with the ability to write clean, functional code • Hands-on experience with LLM APIs (OpenAI, Anthropic, Google, or similar) • Familiarity with at least one deep learning framework (PyTorch or TensorFlow preferred) • Basic experience with LLM orchestration frameworks (LangChain, LangGraph, or similar) • Understanding of NLP fundamentals: text processing, embeddings, classification • Experience with common ML models: regression, classification, clustering • Hands-on experience making API calls and working with LLM responses • Knowledge of Git for version control • Ability to write basic tests for your code • Familiarity with vector databases or embedding systems • Basic understanding of cloud computing concepts • Strong problem-solving skills and eagerness to learn • Good communication skills and the ability to work in a team environment • Self-motivated with intellectual curiosity about AI and machine learning
Responsibilities
• Research & Learning - Stay current with foundational LLM concepts, architectures, and developments in the field. Learn about transformer architectures, prompt engineering, and basic model optimization, Explore new LLM capabilities and frameworks under guidance from senior team members. Read and understand research papers and technical documentation • Solution Design & Prototyping - Support the design of AI solutions for business problems using LLMs. Build proof-of-concept applications to test ideas and validate approaches. Prototype LLM-based applications using modern frameworks. Conduct experiments to compare different approaches. Collaborate with team members and learn from subject matter experts • Development & Implementation - Write clean, readable code following team standards and best practices. Develop LLM-based workflows using orchestration frameworks (such as LangChain, LangGraph, or similar). Implement LLM features, including basic prompt engineering, API calls, and structured outputs. Build functional systems that integrate with LLM APIs. Participate in code reviews and learn from feedback. Optimize code for readability and basic performance considerations • Experimentation & Analysis - Design simple experiments to test ideas and measure results. Evaluate LLM outputs and system performance using established metrics. Work on prompt optimization and basic fine-tuning approaches. Conduct data analysis and statistical testing
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