Are you ready to revolutionize the world of AI-driven information retrieval and work at the cutting edge of language model technology? Join our team as an LLM/Agentic System Engineer, where you'll collaborate with the best in the industry to build innovative solutions that empower businesses worldwide. This is a unique opportunity to push the boundaries of LLM fine-tuning, QLoras, Loras, RAG, and knowledge graph integration. If you're passionate about combining technology with advanced AI techniques and eager to make a significant impact, we want you to be part of our groundbreaking journey!
What we're looking for
We are seeking a highly skilled and innovative LLM/Agentic System Engineer who is an expert in fine-tuning LLMs and integrating them with knowledge graphs. The ideal candidate will have a deep understanding of and extensive experience with LLMs, QLoras, Loras, RAG, and optimizing LLMs for better performance and retrieval. Key qualifications include:
Expertise in LLM Fine-Tuning: Proven ability to fine-tune large language models (LLMs) for various applications.
Proficiency in Python: Advanced proficiency in Python programming, with a strong portfolio demonstrating the development of LLM-based solutions.
Experience with Knowledge Graphs: Extensive hands-on experience interfacing LLMs with knowledge graphs for enhanced information retrieval.
Community Engagement: Active participation in relevant ML and AI communities, staying abreast of the latest advancements and contributing to discussions and developments.
Optimization and Innovation: A knack for optimizing LLM performance and integrating new techniques to push the boundaries of what's possible in AI-driven solutions.
If you are a wizard in fine-tuning LLMs and integrating them with knowledge graphs and eager to contribute to cutting-edge projects in a collaborative and forward-thinking environment, we want to hear from you!
Responsibilities
Fine-Tune LLMs: Fine-tune large language models to meet specific performance criteria and business objectives.
Optimize LLM Performance: Optimize LLMs such as QLoras, Loras, and RAG for accuracy, efficiency, and scalability.
Integrate with Knowledge Graphs: Interface LLMs with knowledge graphs and semantic databases to improve information retrieval and contextual understanding.
Collaborate with Experts: Work closely with AI researchers, data scientists, and domain experts to design and implement robust AI solutions.
Stay Current with Advancements: Continuously monitor and integrate the latest advancements in machine learning and natural language processing.
Community Engagement: Actively participate in ML and AI communities, contributing insights and staying informed about emerging trends and technologies.
Develop Algorithms: Develop and deploy innovative algorithms and techniques to enhance LLM capabilities in real-world scenarios.
Provide Technical Leadership: Provide technical leadership and mentorship to junior engineers, fostering a culture of innovation and excellence within the team.
Documentation and Training: Develop comprehensive documentation and training materials to support users in effectively utilizing AI tools.
Troubleshoot and Support: Provide technical support and troubleshooting for any issues related to LLMs and knowledge graph integration.
Conduct Research and Development: Engage in R&D to explore new techniques and methodologies that can enhance AI capabilities.
Collaborate with Development Teams: Work alongside other developers to ensure the seamless integration of AI workflows within the overall architecture of the solutions.
User Feedback Integration: Gather and analyze user feedback to continuously improve the functionality and user experience of AI tools.
Maintain Code Quality: Ensure high standards of code quality, including writing clean, maintainable code and conducting regular code reviews.
Qualifications
Advanced Degree: Bachelor's or Master’s degree in Computer Science, Machine Learning, or a related field.
Experience with LLMs: Proven experience in fine-tuning and optimizing large language models.
Proficiency in Python: Advanced proficiency in Python, with a strong portfolio of projects demonstrating expertise in ML and AI.
Hands-on Experience with Knowledge Graphs: Extensive experience interfacing LLMs with knowledge graphs and semantic databases.
Machine Learning Expertise: Deep understanding of machine learning principles, algorithms, and techniques, particularly in the context of language models.
Creative Problem-Solving: Strong creative and analytical problem-solving skills, with the ability to innovate and push the boundaries of current capabilities.
Community Engagement: Active participation in relevant ML and AI communities, staying informed about the latest advancements and contributing to discussions and developments.
Collaboration Skills: Excellent interpersonal and communication skills, with a proven ability to work effectively in a team-oriented environment.
Performance Optimization: Experience in optimizing the performance of ML models, ensuring efficiency and scalability.
Technical Documentation: Ability to develop comprehensive documentation and training materials.
Troubleshooting Skills: Strong troubleshooting and problem-solving abilities, with experience in providing technical support and resolving complex issues.
Research and Development: Experience in conducting research and development to explore new techniques and methodologies.
User-Centric Approach: A user-centric approach to design and development, with experience in gathering and integrating user feedback.
Code Quality: Commitment to maintaining high standards of code quality, including writing clean, maintainable code and conducting regular code reviews.