[전문연구요원] Machine Learning Engineer – LLM/ RAG
마감기한
2025년 04월 30일부문
R&D직군
DATA직무
Machine Learning Engineer경력사항
무관 고용형태
병역특례근무지
강남서울특별시 강남구 강남대로 318, 13층, 14층
Roles and Responsibilities
- RAG Model Research & Development
- Research, design, and implement cutting-edge Retrieval-Augmented Generation algorithms and methodologies.
- Continuously evaluate and improve model effectiveness and accuracy.
- Large Language Models (LLM) Optimization
- Fine-tune and optimize state-of-the-art LLMs to enhance retrieval performance and relevance.
- Develop methods to reduce latency and improve model responsiveness in production environments.
- Semantic Search & Vector Database Integration
- Develop and enhance semantic search capabilities, utilizing our novel vector databases solution
- Optimize retrieval mechanisms for maximum performance and scalability.
- Data Management & Pipeline Development
- Create and manage data pipelines for training, evaluating, and continuously updating RAG models.
- Ensure high-quality training datasets through advanced data preprocessing and cleaning techniques.
- SaaS Application Integration
- Lead the integration of RAG and LLM models into production SaaS solutions.
- Collaborate closely with software engineering teams to ensure seamless deployment and scalability.
Basic Qualifications
- Bachelor's or Master's degree in Computer, Machine Learning, or a related field.
- Extensive hands-on experience with Large Language Models.
- Deep expertise in Retrieval-Augmented Generation (RAG) and semantic search.
- Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Strong foundation in data structures, algorithms, and distributed computing.
- Effective collaboration, communication, and problem-solving skills
Preferred Qualifications
- Advanced degree (PhD preferred) in Computer Science, Machine Learning, or a related field.
- Contributions to research publications or open-source communities focused on NLP, RAG, or related fields.
- Experience optimizing models for deployment on cloud platforms (AWS, GCP, Azure).
- Familiarity with MLOps practices, CI/CD pipelines, and model monitoring.
공유하기
[전문연구요원] Machine Learning Engineer – LLM/ RAG
Roles and Responsibilities
- RAG Model Research & Development
- Research, design, and implement cutting-edge Retrieval-Augmented Generation algorithms and methodologies.
- Continuously evaluate and improve model effectiveness and accuracy.
- Large Language Models (LLM) Optimization
- Fine-tune and optimize state-of-the-art LLMs to enhance retrieval performance and relevance.
- Develop methods to reduce latency and improve model responsiveness in production environments.
- Semantic Search & Vector Database Integration
- Develop and enhance semantic search capabilities, utilizing our novel vector databases solution
- Optimize retrieval mechanisms for maximum performance and scalability.
- Data Management & Pipeline Development
- Create and manage data pipelines for training, evaluating, and continuously updating RAG models.
- Ensure high-quality training datasets through advanced data preprocessing and cleaning techniques.
- SaaS Application Integration
- Lead the integration of RAG and LLM models into production SaaS solutions.
- Collaborate closely with software engineering teams to ensure seamless deployment and scalability.
Basic Qualifications
- Bachelor's or Master's degree in Computer, Machine Learning, or a related field.
- Extensive hands-on experience with Large Language Models.
- Deep expertise in Retrieval-Augmented Generation (RAG) and semantic search.
- Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Strong foundation in data structures, algorithms, and distributed computing.
- Effective collaboration, communication, and problem-solving skills
Preferred Qualifications
- Advanced degree (PhD preferred) in Computer Science, Machine Learning, or a related field.
- Contributions to research publications or open-source communities focused on NLP, RAG, or related fields.
- Experience optimizing models for deployment on cloud platforms (AWS, GCP, Azure).
- Familiarity with MLOps practices, CI/CD pipelines, and model monitoring.